Roche Posted October 6, 2026

Post Doctorate Scientist

Basel, Basel-City, Switzerland FULL_TIME
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Roche is the source of truth for this posting and owns the application process. We surface normalized context and market comparison you won't find on the original listing.

About this opportunity

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections,  where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

The Position

The ML for Biosystems Engineering group led by Jonas Fleck at the Institute of Human Biology (IHB) in Basel, Switzerland is seeking a Postdoctoral Researcher to develop machine learning methods for organoid phenotyping and high-throughput screening. Join us to develop next-generation computational methods for drug discovery with complex human model systems.

The Opportunity

You will lead a research project developing computational methods to address important challenges in organoid engineering and drug discovery. Working in a highly collaborative environment with outstanding computational and experimental scientists, you'll develop state-of-the-art statistical and machine learning methods to tackle challenging questions in human biology and disease.

You'll have the chance to work with rich, high-content datasets from complex human model systems, such as large-scale perturbation experiments with multi-modal readouts. In collaboration with experimental scientists at IHB, you will also have the opportunity to shape future experiments, enabling you to develop methods with direct translational impact.

Possible research areas may include:

Causal inference and mechanism discovery from perturbation experiments

Predictive modeling and analysis for high-throughput screening

Computational methods for quantitative comparison of spatial, genomic, and other molecular profiles from organoid models with patient data.

Your work will connect computational innovation with experimental applications, contributing to the development of next-generation human model systems for drug discovery. You will publish your research, contribute open-source tools to the broader scientific community, and gain exposure to drug discovery and development while building your scientific independence as a computational researcher.

Who You are:

PhD in computational biology, computer science, machine learning, bioinformatics, or related technical fields.

Track record of conducting impactful independent research, demonstrated through publications, preprints, or substantial research software contributions.

Strong research experience in statistical or machine learning methods, ideally applied to genomics or other high-dimensional biological data.

Proficiency in Python and experience with modern ML and scientific computing frameworks such as JAX, PyTorch or TensorFlow.

Strong scientific programming and software engineering practices, including version control, testing, reproducible research workflows, and agentic development tools.

Creativity, scientific curiosity, and enthusiasm for developing computational methods that address challenging problems in biomedical research.

Ability and motivation to work closely with experimental scientists and contribute to collaborative, interdisciplinary projects.

Nice to have:

Experience applying machine learning to biomedical data, including genomics, imaging, spatial, or multimodal datasets.

Experience with perturbation experiments, causal inference, high-throughput screening, or predictive modeling.

Contributions to open-source software or scientific computing tools.

Experience working closely with experimental collaborators.

Technical details:

To be considered, your application needs a CV (including a list of relevant publications) and a cover letter describing your research interests.

About the Institute of Human Biology (IHB) & Basel:

The Institute of Human Biology (IHB) is a research center in Basel, Switzerland, dedicated to engineering advanced human model systems for drug discovery, development, and precision medicine. The IHB fosters an interdisciplinary environment bridging academic and pharmaceutical research, connecting biologists, engineers, and data scientists. It has close ties to Roche's Pharmaceutical Research and Early Development (pRED) organization and Genentech (gRED), and collaborates with leading academic institutions including ETH Zürich, University of Basel, and EPFL, as well as globally. Basel is an international hub for research and innovation, cultivating training environments through institutions like the University of Basel and ETH Zürich's Department of Biosystems Sciences and Engineering, creating a melting pot of research development and commercialization.

We are looking forward to your application!

 

 

Who we are

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.

Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.

Job details

Seniority
Not listed
Function
Drug Discovery & Preclinical Research
Therapeutic area
Not listed
Location
Basel, Basel-City, Switzerland
Employment type
FULL_TIME

How this role compares

Computed from every other active Drug Discovery & Preclinical Research role in our database, not just this employer's listings.

We currently track 913 comparable Drug Discovery & Preclinical Research roles across 88 biopharma companies.

913Comparable roles tracked
851Currently active
88Companies hiring similar roles
32Countries represented

Salary context

355 of 913 peers report a salary range (USD, annualized)

Peers share this role's job function. This posting doesn't list a seniority level, so peers aren't narrowed by seniority either -- the range below may span more levels than usual.

This roleSubject Not listed on this posting
Highest disclosed · Vice President, Therapeutics Immunology · ModernaTX, Inc. $272,200/yr – $490,100/yr
Peer group range $0 – $381,150 (median $155,000)

Where these roles are based

Top locations among the 913 comparable roles

United States627
China46
Germany37
Switzerland29
United Kingdom28
France27

+ 26 more countries

Seniority mix

655 of 913 peers have a known seniority level

Senior216
Principal149
Intern/Fellow/Postdoc70
Associate61
Director61
Associate Director32
Manager27
Senior Director24
Executive/VP15

Therapeutic area mix

124 of 913 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden

Immunology50
Oncology43
Neuroscience15
Cardiovascular / CVRM9
Respiratory3
Vaccines & Infectious Disease2
Rare Disease2

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The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.

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How we calculate "similar"

No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.

Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.

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Unmatched or unknown dimensions score exactly the same: 0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.