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Scientist - Computational & Applied AI

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описание

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.

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations (gRED and pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CS-CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

Within this newly established Center of Excellence, our activities operate across a distinct continuum of innovation and explainability. The Computational Biology and Medicine team stands as the vital final link in this continuum. Focussed on our delivery for the portfolio we bridge advanced algorithmic models with deep mechanistic validation to turn AI-driven hypotheses into validated therapeutic targets and deepen our understanding of disease biology and our patients.

задачи

The Senior Computational and Applied AI Scientist within the Computational Biology and Medicine team is responsible for developing and deploying translational and applied AI frameworks to accelerate our Immunology and Cardiovascular, Renal, and Metabolism (CVRM) portfolios. Operating at the final stage of translational execution for Discovery and Early Development, you will focus on moving beyond pure statistical correlation toward a systems-level, mechanistic understanding of disease and target biology. This position offers a trajectory of professional development aligned with scientific excellence and modern enterprise productivity frameworks ensuring our computational pipelines directly optimize R&D throughput and compress discovery cycles.

Working across departments to co-develop and implement advanced machine learning models focused on explainable and causal AI methods (such as causal models, symbolic regression, and counterfactual inference) to map disease pathways and unlock true mechanistic target validation.

Adaptation and fine-tune state-of-the-art biological foundation models to predict novel therapeutic targets in complex immune diseases and CVRM.

Bridging the translational gap by implementing adversarial domain adaptation and cross-species translation algorithms to dynamically align animal model phenotypes with human patient endotypes.

Leveraging patient-level multi-omics, spatial profiles, and real-world clinical datasets to identify distinct disease endotypes, defining mechanistic patient selection criteria that enable clinical scientists to stratify responder vs. non-responder populations.

Constructing dynamic representations and physics-informed models (e.g., Neural ODEs, PINNs) to capture physiological kinetics, temporal disease progression, and patient "digital twins" entirely in silico.

Applying graph-based deep learning approaches, building and querying large-scale biological knowledge graphs to capture systems-level interactions across complex tissue-remodeling and auto-inflammatory networks.

Anchoring the final stage of the organizational continuum, translating algorithmic innovations and "ML-ready" frameworks directly into high-value portfolio deliverables.

Collaborating cross-functionally with clinical scientists, wet-lab disease biology experts, biostatisticians, and enterprise engineers across pRED and gRED to convert ambiguous therapeutic questions into concrete computational pipelines.

Ensuring provenance and explainability is an integral feature of your insight delivery to stakeholders

требования

You hold a PhD (preferred) or MSc in Computational Biology, Computer Science, Machine Learning, Bioinformatics, Systems Biology, Artificial Intelligence, or a highly quantitative field.

You bring 3+ years (Senior Scientist) or 6+ years (Principal Scientist) of post-doctoral or biopharmaceutical industry experience delivering applied AI and data science solutions in a drug-discovery setting.

You possess hands-on experience applying causal inference toolkits, graph neural networks, or generative models to high-dimensional biomedical data (single-cell, spatial omics, or clinical trial datasets).

You possess experience adapting large-scale biological foundation models or building dynamic systems models to capture temporal disease progression.

You demonstrate an active interest or deep domain understanding of human immunology, complex and multifactorial diseases, or system-level pathophysiology.

You exhibit elite communication and teamwork skills, with a proven track record of acting as a cross-functional catalyst between pure computational engineers and wet-lab experimentalists.

This position is based in Basel, Switzerland

условия

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.

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