The University of Basel’s High Performance Computing (HPC) research group, led by Professor Florina M. Ciorba in the Department of Mathematics and Computer Science, is offering a fully funded PhD position as part of the SNF Bridge Discovery project “Family Gene Toolkit: A digital service to support genetic care in Europe.” This interdisciplinary project brings together experts in HPC, nursing, social sciences, and 14 clinical partners across Switzerland to advance digital tools for families affected by Hereditary Breast and Ovarian Cancer (HBOC) and Lynch Syndrome (LS). The next-generation Family Gene Toolkit (FGT v3.0) will integrate cutting-edge AI and confidential computing to personalize patient care and support clinicians in long-term monitoring and decision-making. As a PhD candidate, you will be responsible for designing and developing the secure technical foundation for FGT v3.0. Your work will include building confidential HPC pipelines, scalable training infrastructure, and fine-tuning large medical language models (LLMs) using clinical guidelines, evidence-based datasets, and real-world medical corpora. You will rigorously evaluate these models with established medical and multilingual benchmarks, and contribute to the development of prototype chatbot and retrieval systems for clinical partners. Integration of medical LLMs with multilingual models and contributions to robustness and confidential-AI evaluation workflows are also key aspects of the role. Research activities will be closely aligned with the project’s objectives, and you will be expected to publish high-quality papers in leading conferences and journals in HPC and AI for healthcare. You will present your results at seminars, workshops, and international conferences, and actively engage with the HPC group and partner institutions. Additional responsibilities include assisting with teaching (one class per semester) and supervision (one student or more per semester). The position is fully funded for four years (48 months) according to SNSF guidelines, with a gross annual salary of approximately CHF 50,000 and generous travel funds for international conferences. You will have access to powerful supercomputers, close research mentoring, and excellent working conditions in a stimulating and supportive environment. Eligibility requirements include a Master’s degree in Computer Science, Engineering, or a closely related field by March 2026, an excellent academic record, programming skills in C, C++, Java, or Python, experience with parallel programming, Linux, machine-learning frameworks, and privacy-enhancing technologies, and fluency in English. Strong communication, problem-solving skills, and a collaborative mindset are essential, along with genuine curiosity and motivation for impactful research in computer science and healthcare. To apply, submit a single PDF file containing your CV (with publication list), Bachelor’s and Master’s theses, at least one relevant publication (if available), transcripts, links to personal software contributions, a motivation statement, and contact information for 1-2 professors willing to provide recommendation letters. Applications are encouraged before 14 January 2026 via the provided application link. Evaluation begins 19 January 2026 and continues until the position is filled. Short-listed applicants will be contacted within three weeks to arrange an interview. For specific questions, contact Professor Florina M. Ciorba at [email protected].

The University of Basel’s High Performance Computing (HPC) research group, led by Professor Florina M. Ciorba in the Department of Mathematics and Computer Science, is offering a fully funded PhD position as part of the SNF Bridge Discovery project “Family Gene Toolkit: A digital service to support genetic care in Europe.” This interdisciplinary project brings together experts in HPC, nursing, social sciences, and 14 clinical partners across Switzerland to advance the Family Gene Toolkit (FGT), a digital platform supporting families affected by Hereditary Breast and Ovarian Cancer (HBOC) and Lynch Syndrome (LS). The next phase, FGT v3.0, will integrate cutting-edge AI and confidential computing to personalize patient care and improve clinical outcomes. As a PhD candidate, you will be responsible for building the secure technical foundation for FGT 3.0, including confidential HPC pipelines, scalable training infrastructure, and fine-tuned medical large language models (LLMs) trained on clinical guidelines, evidence-based datasets, and real-world medical corpora. You will rigorously evaluate these models using established medical and multilingual benchmarks and contribute to the development of prototype chatbot and retrieval systems for clinical partners. Your research will focus on designing, developing, and integrating AI-based and privacy-preserving features into FGT v3.0 within a confidential HPC environment. Key tasks include training and fine-tuning large medical LLMs (up to 70B parameters) on confidential HPC systems, preparing medical datasets (Clinical Guidelines, NCCN, PubMed, MIMIC) for secure LLM training, developing high-performance pipelines for TEE-based model training and inference, benchmarking models, and integrating medical LLMs with multilingual models (Apertus). You will also contribute to robustness and confidential-AI evaluation workflows. Additional responsibilities include conducting research aligned with project objectives, writing and submitting high-quality papers to leading conferences and journals in HPC and AI for healthcare, presenting results at seminars, workshops, and international conferences, and actively collaborating within the HPC group and across partner institutions. You will assist with teaching (one class per semester) and supervision (one or more students per semester). Eligibility requirements include a Master’s degree in Computer Science, Engineering, or a closely related field by March 2026, an excellent academic record, programming skills in C, C++, Java, or Python, experience with parallel programming, Linux, machine-learning frameworks, and privacy-enhancing technologies, fluency in English, and strong communication and problem-solving skills. A genuine curiosity for Computer Science and motivation to conduct impactful research in healthcare are essential. The position offers 100% funding per SNSF guidelines (currently approx. CHF 50,000 gross/year), generous travel funds for international conferences, access to powerful supercomputers, close research mentoring, and excellent working conditions in a stimulating and supportive environment. The start date is April 2026, and the position duration is four years. To apply, submit a single PDF file containing your CV (with publication list), Bachelor’s and Master’s theses, at least one relevant publication (if available), transcripts, links to personal software contributions, a motivation statement, and contact information for 1-2 professors willing to provide recommendation letters. Applications are encouraged before 18 January 2026. Evaluation begins 19 January 2026 and continues until the position is filled. For specific questions, contact Prof. Florina M. Ciorba at [email protected]. Application link: Apply here

The High Performance Computing (HPC) research group, led by Professor Florina M. Ciorba in the Department of Mathematics and Computer Science at the University of Basel, Switzerland, is offering a fully funded PhD position as part of the SNF Bridge Discovery project “Family Gene Toolkit: A digital service to support genetic care in Europe.” This collaborative project brings together experts from Nursing (Prof. Maria C. Katapodi, University of Basel), Social Sciences and Health Issues (Prof. Maria Caiata-Zufferey, SUPSI), and High Performance Computing (Prof. Florina M. Ciorba, University of Basel), along with 14 clinical partners across Switzerland. The Family Gene Toolkit (FGT) is a digital platform designed to provide reliable information and support for families affected by Hereditary Breast and Ovarian Cancer (HBOC) and Lynch Syndrome (LS). The platform prepares families for clinical consultations and shared decision-making, while also assisting clinicians in organizing long-term care and monitoring. The next phase, FGT v3.0, will integrate advanced AI technologies to enhance personalization for patients and improve clinical efficacy. The selected PhD candidate will play a key role in building the secure technical foundation for FGT 3.0. Responsibilities include developing confidential HPC pipelines, scalable training infrastructure, and fine-tuning medical large language models (LLMs) using clinical guidelines, evidence-based datasets, and real-world medical corpora. The models will be evaluated with established medical and multilingual benchmarks to ensure robustness and clinical relevance. This position is ideal for candidates with a strong background in artificial intelligence, security, and/or high performance computing, and a clear motivation to apply privacy-preserving and efficient computational methods to AI in genetic healthcare. The project offers a unique opportunity to work at the intersection of computer science, medical science, and clinical practice, contributing to the advancement of digital health platforms for cancer care. The position is fully funded for 4 years (48 months) by the SNF Bridge Discovery project. The anticipated start date is April 2026. Applicants should hold a relevant degree and demonstrate experience or strong interest in HPC, AI, and secure computation. No specific GPA or language test requirements are mentioned. To apply, visit the University of Basel job portal using the provided application link. Prepare your CV and supporting documents, and follow the instructions for submission. For further details, contact the research group or supervisors.