Naučna karijera

About the Programme

Our goal is to give you a real sense of what it’s like to work at Jane Street full time. Over the course of your internship, you will explore ways to approach and solve exciting problems within your field of interest through fun and challenging classes, interactive sessions, and group discussions—and then you will have the chance to put those lessons to practical use.

As an intern, you are paired with full-time employees who act as mentors, collaborating with you on real-world projects we actually need done. When you’re not working on your project, you will have plenty of time to use our office amenities, physical and virtual educational resources, attend guest speakers and social events, and engage with the parts of our work that excite you the most.

If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind, a collaborative spirit, and a passion for solving interesting problems, we have a feeling you’ll fit right in.

About the Position

As a Software Engineering intern, you’ll learn how we use OCaml (our primary development language) in our day to day work, and gain exposure to the libraries and tools that are foundational to our internal systems.

During the programme you’ll work on two projects, mentored closely by the full-time employees who designed them. Some projects consider big-picture questions that we’re still trying to figure out, while others involve building something new. Your mentors will work in two distinct areas, so you’ll gain a better understanding of the wide range of problems we solve every day, from high performance trading systems to programming language design and everything in between.

If you’d like to learn more, you can read about our interview and team placement processes and get a sense of what some of our recent intern projects looked like.

About You

We don’t expect you to have a background in finance, OCaml, functional programming, or any other specific field—we’re looking for smart people who enjoy solving interesting problems. We’re more interested in how you think and learn than what you currently know. You should be:

A top-notch programmer with a passion for technology Collaborative and courteous with strong interpersonal and communication skills Eager to ask questions, admit mistakes, and learn new things Fluent in English

If you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.

We're always looking for talented people to join our business, which is why we don't have specific roles on our careers page for people to apply to. If you are interested in learning more about opportunities at Quadrature, then send us your CV and a Cover Letter telling us more about what kind of work you'd like to do.

Note that we receive a high number of applications so we may not be able to respond individually to these. If your experience is relevant for us, a member of our talent team will reach out to organise an introduction call.

Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.

The quantitative trading teams at Jump Trading probe and examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis, machine learning and deep learning skills, using the results of their research to make forecasts and develop profitable predictive trading models.

What You’ll Do:

We are seeking research scientists with a demonstrated ability to apply deep learning to achieve state-of-the-art capabilities in complex and challenging domains. The ideal person for this role will be capable of implementing an open-ended research project from concept to production and continuously improving model design, tools, and infrastructure. Potential projects may target any area of the quantitative research and monetization process. We believe that successful research efforts require a fluid mix of skills including ML expertise, engineering pragmatism, statistics and market intuition.

Other duties as assigned or needed.

Skills You’ll Need:

5+ years of experience in developing DL systems with measurable impact in industry and/or academia Creative thinkers who are driven, self-motivated, and eager to solve challenging problems Proficiency in Python and/or C++ Strong foundation in mathematics and statistics Ability to thrive in a collaborative, team-oriented environment PhD, or Master's degree in Computer Science, Mathematics, (or related subject) Strong publications record at ICML, ICLR, AAAI, NeurIPS, UAI, KDD, or equivalent Reliable and predictable availability Excellent written and verbal communication skills in English

Benefits

Discretionary bonus eligibility Medical, dental, and vision insurance HSA, FSA, and Dependent Care options Employer Paid Group Term Life and AD&D Insurance Voluntary Life & AD&D insurance Paid vacation plus paid holidays Retirement plan with employer match Paid parental leave Wellness Program

Department Summary

Clinical Pharmacology is a dynamic function that plays a critical role in supporting drug development teams and in optimizing the dosing regimen for patients. Utilizing our expertise in pharmacokinetics and pharmacodynamics, Clinical Pharmacologists contribute at every stage of development, applying quantitative pharmacology principles across a molecule\'s life-cycle for both small molecules, biologics, and novel treatment modalities. The Clinical Pharmacology department is instrumental in shaping future treatments and ensuring they are both safe and effective.

This internship position is located in South San Francisco, on-site.

The Opportunity

The key responsibilities may include, but are not limited to:

Participate in Clinical Pharmacology research projects supporting drug development, including evaluation of exposure–response relationships for safety and/or efficacy endpoints, assessment of patient- and disease-related factors that may influence risk or cause confounding, and support of dose-selection or dose-optimization strategies. Activities may involve analysis of internal clinical data, review of relevant literature and regulatory documents, and synthesis of findings across studies.

To address Clinical Pharmacology questions and better understand sources of variability and confounding in drug response, develop and apply quantitative modeling approaches, such as exposure-response analyses, statistical analysis, Quantitative Systems Pharmacology (QSP) modeling, or emerging Machine Learning (ML) methodologies.

Formulate scientifically sound conclusions and communicate results through clear documentation, presentations, and discussions with cross-functional stakeholders.

Program Highlights

Intensive 12-weeks, full-time (40 hours per week) paid internship. Program start dates are in May/June 2026. A stipend, based on location, will be provided to help alleviate costs associated with the internship. Ownership of challenging and impactful business-critical projects. Work with some of the most talented people in the biotechnology industry.

Who You Are (Required)

Required Education:

You meet one of the following criteria: Must be pursuing a Master\'s Degree (enrolled student). Must have attained a Master\'s Degree. Must be pursuing a PhD (enrolled student).

Required Majors: Quantitative disciplines (Statistics/Biostatistics, Computational Biology/Chemistry, Chemical/Biomedical engineering) or related field.

Required Skills:

Proficiency in R or Python for statistical modeling and data visualization.

Causal interference analysis, G-computation, and Propensity score matching.

Preferred Knowledge, Skills, and Qualifications

Excellent communication, collaboration, and interpersonal skills.

Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Relocation benefits are not available for this job posting.

The expected salary range for this position based on the primary location of California is $50.00 hour. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company\'s policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity.

From our London HQ, we unite world-class researchers and engineers in an environment that values deep exploration and methodical execution - because the best ideas take time to evolve. Together we’re building a world-class platform to amplify our teams’ most powerful ideas.

Join a research team where curiosity meets scale. You’ll investigate foundational questions, uncover market insights and push the boundaries of what's possible - all with the support of near-limitless compute and world-class peers.

Take the next step in your career.

The role

10-week summer programme (22nd June to 28th August 2026) 09:00-17:30 working hours Based in Central London

Over the course of 10 weeks, G-Research Summer Research Programme interns gain a unique insight into life as a Natural Language Processing (NLP) practitioner at a leading quantitative research firm.

Our full-time NLP researchers use a wide range of tools and techniques in an applied setting, putting their expertise to use in direct, production-ready applications with immediate results. They have access to vast computing resources and are limited only by their imagination.

As an NLP intern, you will have the opportunity to experience this as part of a 10-week programme working on a meaningful and challenging research project that demands the application of innovative yet pragmatic mathematical and computational analysis.

You will be paired with a mentor who will supervise your work and provide ongoing feedback to help you improve and develop, as well as access to senior staff who are leaders in their fields. Your internship will culminate in a final presentation of your research ideas to senior management.

Taking part in G-Research's Summer Internship Programme will give you an in-depth insight into our academic approach to the world of quantitative research and allow you to explore the thriving city of London, while you get to know your fellow interns and colleagues through a full itinerary of fun social events.

Top performers on the programme will be considered for full-time opportunities on completion of their studies.

Who are we looking for?

The ideal candidate will, at a minimum, have experience in the following areas:

A post-graduate degree in Natural Language Programming or a related discipline, or commercial experience developing novel machine learning algorithms. We will also consider exceptional candidates with a proven record of success in online data science competitions, such as Kaggle. PhD level study is preferred Experience working with large-scale, noisy text datasets and building models for information extraction, sentiment analysis or semantic understanding Publications at top-tier NLP or machine learning venues, such as ACL, EMNLP, NAACL, NeurIPS and ICML are advantageous Strong coding skills in Python and experience with modern ML frameworks, such as PyTorch and TensorFlow Excellent reasoning skills and mathematical ability are crucial: off-the-shelf methods don't always work with our data, so you will need to understand how to develop your own models

Previous experience in finance is not required, although an interest in finance and the motivation to rapidly learn more is a prerequisite for working here.

Why should you apply?

Highly competitive compensation plus accommodation G-Research community with weekly intern activities Lunch provided (via Just Eat for Business) and dedicated barista bar 30 days’ annual leave pro-rated Informal dress code and excellent work/life balance Central London office close to 5 stations and 6 tube lines

This will be a 2-year fixed-term position with a possibility for extension based on performance, the availability of funding and programmatic needs.

From benchtop research to international programs, the Department of Surgery delivers exceptional patient care, provides outstanding undergraduate and graduate education, and advances the field of surgery through innovative basic science and clinical research.

Our department is a high-performing team of dedicated faculty and staff, committed to setting ambitious goals that foster collaboration and drive growth in education, research, clinical training, and innovation—elevating the profile of Stanford Surgery and advancing our mission worldwide.

The laboratory of Dr. Sheri Krams at Stanford School of Medicine (Department of Surgery, Division of Abdominal Transplantation) is recruiting a Basic Life Research Scientist to study core mechanisms of immune recognition and regulation in clinically relevant settings. Our work dissects the cellular and molecular pathways that shape immune responses to alloantigen and Epstein–Barr virus (EBV), with a growing focus on how extracellular vesicles mediate intercellular communication and modulate immune function in transplantation and viral immunity—linking fundamental immune circuitry to the biology underlying transplant-associated immune complications.

A major emphasis of the lab is defining the diversity, regulation, and functional specialization of natural killer (NK) cells in response to allografts and xenografts. This is an opportunity to join a collaborative program that blends discovery-driven immunology with access to human samples and disease context, enabling mechanistic insights with clear potential to inform future therapeutic strategies.

CORE DUTIES:

The candidate should have experience, in or be willing to learn, CyTOF, sequencing-including NGS and RNA-Seq, and bioinformatics. The candidate must be quick to learn new techniques and be able to modify and adapt standard protocols. The candidate should be organized, display good judgment, have excellent communication skills and be a team player. The candidate will be expected to write and contribute to research protocols, manuscripts and grants.

The candidate will also be responsible for supervising, training, and mentoring graduate students, postdoctoral fellows, undergraduate students, medical students, clinical fellows, and visiting scientists and participate in collaborations with other research groups.

QUALIFICATIONS/MINIMUM REQUIREMENTS:

Ph.D. and/or M.D. degree, and at least 5 years of postdoctoral experience. Strong record of productivity and publications. Course work and/or published research in Immunology. Must be self-motivated, adaptable, and able to work with minimal direction. Strong organizational, collaborative, and time management skills. Strong ability to write and communicate effectively. Excellent written and oral skills with experience writing medical education research papers. Ability to work independently and part of the departments and medical school’s medical education research teams.

The expected pay range for this position is $92,235 to $110,000 per annum.

Stanford University provides pay ranges representing its good faith estimate of the salary or hourly wage the university reasonably expects to pay for a position upon hire. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location, and external market pay for comparable jobs.

At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website (https://cardinalatwork.stanford.edu/benefits-rewards) provides detailed information on Stanford’s extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.

Applications are invited for the post of Associate Professor (or Professor) in Theoretical Biophysics in the Rudolf Peierls Centre for Theoretical Physics, Department of Physics, University of Oxford. The successful candidate will also be appointed to a Tutorial Fellowship at St Hilda’s College under arrangements described in the Job Description and Person Specification. The appointment will be initially for five years at which point, upon completion of a successful review, the post-holder will be eligible for reappointment to the retiring age.

The Associate Professor will develop a world-leading research programme in theoretical biophysics, (widely interpreted to include overlapping areas such as statistical physics, soft matter and the physics of life), teach at undergraduate and graduate level, and participate in administration. On behalf of the College they will have responsibility for teaching undergraduates reading for degrees in physics, and acting as a pastoral advisor to graduate students. They will play a role in the running of the College as a charity trustee and a member of its Governing Body.

The successful candidate will hold a doctorate in theoretical physics or a related subject, and will have a proven record of high quality, creative research at an international level. They will be an excellent teacher of undergraduates and graduates and have the interpersonal skills necessary to engage with students and colleagues at all levels.

Applications are particularly welcome from women and black and minority ethnic candidates, who are under-represented in academic posts in Oxford. All applicants will be judged on merit, according to the selection criteria.

If you would like to discuss this post and find out more about joining the academic community at Oxford, please contact Professor Ard Louis at ard.louis@physics.ox.ac.uk , Professor Alex Mietke alexander.mietke@physics.ox.ac.uk, or Professor Julia Yeomans julia.yeomans@physics.ox.ac.uk. Queries about the college side of the appointment should be addressed to the Senior Tutor, Dr Sarah Norman, at sarah.norman@st-hildas.ox.ac.uk. All enquiries will be treated in strict confidence and will not form part of the selection decision.

Only applications received by noon (GMT) on Monday, 2 March 2026 can be considered.

Applicants should ensure that their three referees to send their letters before the application deadline, to aptreferences@physics.ox.ac.uk

Applications are invited for two Assistant Professors at the new Bennett School of Public Policy in the Faculty of Human, Social and Political Science (BSPP). The start date for these roles will be July 2026.

This is an exciting opportunity to join the new Bennett School of Public Policy (BSPP) in its infancy https://www.bennettschool.cam.ac.uk/. The BSPP has an ambitious growth plan including launching a new M.Phil in Digital Policy alongside an expanded M.Phil in Public Policy, and the development of a new PhD programme. One post-holder will play a leading role in helping establish a new M.Phil in Digital Policy and also in relation to the existing, highly successful M.Phil Programme in Public Policy. One post-holder will play a leading role in relation to the existing, highly successful M.Phil Programme in Public Policy. They may also teach on the anticipated new M.Phil in Digital Public Policy (from 2026-27).

Candidates will be expected to have a PhD in any relevant discipline. We are looking for scholars with an excellent track record in both research and teaching in any areas of public policy. We are happy to consider applicants from different disciplinary backgrounds.

The successful candidates will have a record of research publications commensurate with the international reputation of the University of Cambridge. The candidate should show an outstanding record of publications, which may include published monographs or monographs nearing publication, that demonstrate a clear research trajectory and promise.

The candidate will be expected to provide at least 40 hours of lectures and seminars annually, supervise M.Phil dissertations, conduct small-group teaching (supervisions) and perform academic administration and examining.

The successful candidates are expected to contribute positively to the teaching of the BSPP. The Department is expanding the existing M.Phil in Public Policy and launching a new M.Phil in Digital Policy, expanding to around 100 students in the first instance. A new PhD programme will also be launched. The students are drawn from all over the world and from a wide range of disciplinary backgrounds. The post-holders will: (i) deliver teaching for both M.Phil and the PhD programmes; (ii) take on a range of necessary administrative duties in the Dept; (iii) conduct original research of the highest international standards; (iv) contribute to other teaching where appropriate.

Candidates must be able to show evidence of research of international standing, demonstrate a willingness and ability to contribute collegially to the life of the new BSPP, and possess a flexible approach to teaching and administrative duties.

In order for applications to be considered applicants must upload:

Covering letter, explaining the reasons for your application and how your knowledge, skills and experience match the requirements of the role you are applying for A teaching statement, outlining your experience, interests, and approach to teaching both larger lectures and smaller groups A research statement, outlining your plans for research, scholarship and grant capture over the next 5 years If available two recently-published research articles A Curriculum Vitae (CV), to include full details of educational qualifications and academic experience, a list of publications.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. We particularly welcome applications from candidates from a BAME background for this vacancy as they are currently under-represented at this level in our department.

Further information about the BSPP can be found at: https://www.bennettschool.cam.ac.uk

Applications should be made online. Applicants must provide the names and contact details of three referees who are familiar with their work.

Enquiries about applications should be addressed to Sarah Rosella, recruitment@bennettschool.cam.ac.uk

Please quote reference JB48529 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

The simulation of electronic devices has a long and successful history of accompanying experimental developments, be it for transistors or memory cells. Nowadays, to be of practical relevance, such technology computer aided design (TCAD) tools should operate at the ab-initio and quantum mechanical level. Moreover, they should capture the interplay between electrical (voltage-induced currents), thermal (excitation of crystal vibrations), and structural (migration of atoms) effects with an atomistic resolution. This can be achieved by self-consistently coupling molecular dynamics (MD), density-functional theory (DFT), and quantum transport (QT) simulations of both electrons and phonons.

The Computational Nanoelectronics Group of ETH Zurich recently started implementing a novel, state-of-the-art TCAD tool called QuaTrEx that can perform ab-initio QT calculations at unprecedented scale. As QuaTrEx aims to solve for the transport and interactions of various quanta (electrons, phonons, etc) directly at atomic resolution, it requires ab-initio material inputs corresponding to the simulated device components, such as the Hamiltonian and Dynamical matrices, electron-phonon coupling elements, forces and energies, etc. Computing these inputs for device-scale structures, with methods such as DFT, currently poses a bottleneck in the application's capabilities.

Project background

The Computational Nanoelectronics Group was recently awarded a grant from the Swiss National Science Foundation entitled Machine Learning for Optimized Ab-initio Quantum Transport Simulations (MALOQ). It officially started on January 1st 2026 and will conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical quantities that enter ab-initio QT simulations of nanoelectronic devices. In this context, we are seeking a post-doctoral fellow who will be part of a team that also comprises two PhD students and will closely collaborate with the QuaTrEx developers.

Job description

As part of the MALOQ project, you will train state-of-the-art ML models to learn atomic, electronic, and vibrational properties of large-scale atomic systems representing the building blocks of semiconductor devices. The aim is to predict these properties for arbitrarily large structures, at a DFT-level of accuracy.

As a starting point, you will extend the large-scale equivariant GNNs we develop for Hamiltonian matrix prediction to treat dynamical matrices. This ML framework will then also allow us to produce the derivatives of both quantities, which correspond to the electron-phonon and anharmonic phonon-phonon coupling elements. With these, dedicated scattering rates can be computed and then used in quantum transport simulations. Down the line, we aim to pre-train a common GNN backbone model capable of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a significant computational component in deploying multi-GPU codes to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest.

Your contributions would be across the spectrum from methodological development, implementation, and application to realistic semiconductor device systems made of thousands of atoms. All codes will be made freely available to the scientific community through GitHub.

Profile

A track record in building and deploying ML models for applications in materials research, and willingness to work on both methods development and applications Publications in top ML conferences and/or prominent journals in materials sciences and device physics Enjoy collaborating with other researchers in a friendly environment Be willing to supervise junior PhD and master students

We offer

Your job with impact: Become part of ETH Zurich, which not only supports your professional development, but also actively contributes to positive change in society You can expect numerous benefits, such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ, childcare and attractive pension benefits We offer an exciting and challenging activity in a team of highly motivated physicists, electrical engineers, and computer scientists and a salary according to the standard of ETH Zurich for post-docs The duration of the post-doc can be up to two years. The participation in international conferences and the collaboration with industry and academia is strongly encouraged and supported

Snapshot

Our team, GeminiApp, is on a mission to build a universal AI assistant that will empower billions of people. We are creating a personal, proactive, and powerful life assistant that will be used multiple times a day to increase productivity and creativity by 10 to 100-fold. Our work is shaping how humanity interacts with AI at scale.

About Us

Artificial Intelligence could be one of humanity’s most useful inventions. At DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

The Role

As a Data Scientist on the GeminiApp team, you are a key partner and co-creator in our product strategy while managing a growing team. You will be instrumental in building a uniquely proactive and powerful assistant by ensuring our strategic decisions are grounded in data. This is a high-impact role for a data scientist who is excited about working in a fast-paced, innovative environment and who is passionate about building user-centered experiences that will redefine our relationship with technology.

Key responsibilities:

Partner with Verticals PM, engineering, and UX to develop data-driven product strategies Translate ambiguous questions into well-defined problems, design experiments, and analyze large complex datasets for insights Develop and implement novel, goal-oriented metrics Build and deploy statistical/ML models to understand our users, enhance product capabilities and personalize user experience Communicate findings & recommendations to stakeholders, including executives Champion data-driven culture by feeding user engagement insights back into models Collaborate with the GenAI team on model quality and feature adoption Act as a technical leader for a global team, guiding junior members on complex analyses and upholding best practices to ensure high-quality, impactful work

About You

In order to set you up for success as a Data Scientist at DeepMind,  we look for the following skills and experience:

Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 5 years of experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL) or 2 years of experience with a Master's degree. 2 years of work experience identifying opportunities for business/product improvement and then defining/measuring the success of those initiatives.

In addition, the following would be an advantage:

A bias for action and a relentless drive to build something great. You are a resourceful and creative problem-solver, able to find innovative solutions in a fast-paced and dynamic environment. You are comfortable with ambiguity and thrive on a challenge. Strong business acumen and a strategic mindset. You have a proven ability to connect data-driven insights to business impact and can effectively translate business needs into data science problems. Deep technical expertise. You have a strong foundation in statistical modeling, machine learning, and experimental design. You are proficient in SQL and have hands-on experience with a programming language like Python or R. Exceptional communication and presentation skills. You can articulate complex technical concepts to both technical and non-technical audiences, including executive leadership. You are a compelling storyteller who can use data to influence decisions. A collaborative and influential partner. You have a track record of successfully collaborating with and influencing cross-functional teams, particularly engineering and product management, to drive data-informed decisions. You are ready to work in an environment with a shared roadmap that holds multiple teams accountable for both model quality and feature adoption. A commitment to user trust and privacy. You understand the importance of telling users what data is being used and why, and you are committed to providing users with granular, easy-to-understand controls over their personal information.