Job Description

The High Performance Computing (HPC) research group (led by Prof. Florina M. Ciorba) in the Department of Mathematics and Computer Science at the University of Basel (Switzerland) invites applications for a PhD position funded by the SNF Bridge Discovery project

"Family Gene Toolkit: A digital service to support genetic care in Europe"

(grant number 237643).



The BRIDGE

Family Gene Toolkit

project unites three applicants: Prof. Maria C. Katapodi (Nursing, University of Basel), Prof. Maria Caiata-Zufferey (Social Sciences and Health Issues, SUPSI), and Prof. Florina M. Ciorba (High Performance Computing, University of Basel), and 14 clinical partners across Switzerland.



The project aims to advance the Family Gene Toolkit (FGT) v2.0, a digital platform that offers reliable information and prepares families concerned with Hereditary Breast and Ovarian Cancer (HBOC) and Lynch Syndrome (LS) for clinical consultation and shared decision-making, while helping clinicians organize long-term care and monitoring. FGT v3.0 will integrate technological advancements with AI to enhance personalization for patients and improve clinical efficacy.



The

PhD candidate

will build the secure technical foundation for FGT 3.0: confidential HPC pipelines, scalable training infrastructure, and fine-tuned medical LLMs trained on clinical guidelines, evidence-based datasets, and real-world medical corpora. These models will be rigorously evaluated using established medical and multilingual benchmarks.



The ideal candidate has a background in AI, security, and/or high performance computing, with clear motivation to apply security, privacy-preserving, and efficient computational methods to AI for genetic healthcare.

Start:

April 2026

.

Duration: 4 years (48 months).

Your position

You will design, develop, and integrate AI-based and privacy-preserving features into FGT v3.0 within a confidential HPC environment, including:

Train and fine-tune large medical LLMs (70B-scale) on confidential HPC systems. Prepare medical datasets (Clinical Guidelines, NCCN, PubMed, MIMIC) for secure LLM t- raining. Develop high-performance pipelines for TEE-based model training and inference. Benchmark models using standard medical and multilingual evaluation suites. Build prototype chatbot and retrieval systems for clinical partners. Integrate medical LLMs with multilingual models (Apertus). Contribute to robustness and confidential-AI evaluation workflows.


Your tasks also include:

Conduct research aligned with project objectives Write and submit high-quality papers to leading conferences and journals in HPC and AI, for healthcare Present results at seminars, workshops, and international conferences Interact actively, fruitfully, and respectfully within the PI, HPC group, and across partner institutions Contribute to teaching as assistant (one class per semester) Contribute to supervision as assistant (one student or more per semester)

Your profile

Requirements for the position:

Master's degree in Computer Science/Engineering or a closely related field at the start date (March 2026 at the latest) 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 (spoken and written) Clear communication, problem-solving ability, and collaborative mindset Genuine curiosity for Computer Science and motivation to conduct rigorous research with impact in Computer Science and Healthcare

We offer you

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100% funding per SNSF guidelines (currently approx. CHF 50'000 gross/year) Research topics with tangible impact in healthcare and computer science Access to powerful supercomputers Close research mentoring, collaborations, and networking opportunities Excellent working conditions in a stimulating and supportive environment Generous travel funds for international conferences

Application / Contact

Submit a single PDF file containing:

Curriculum vitae, including a publication list (with open-access links, if available) Bachelor's and Master's theses If available, at least one relevant publication (with a brief statement of justification for its selection) Transcripts for Bachelor's and Master's degrees Links to examples of personal software contributions Motivation statement briefly addressing the questions:

1. Why are you applying for this position?

2. What aspects of the project interest you?

3. Which prior experiences best prepare you for this research?

4. What do you aim to achieve during the PhD?

Contact information for 1-2 professors willing to provide recommendation letters (please

do not

include the letters at this stage)


Applications are encouraged

before 18 January 2026

. 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. Due to the anticipated volume of applications, notifications are sent only to short-listed applicants.



For specific questions, contact

Prof. Florina M. Ciorba

(florina.ciorba@unibas.ch).
Apply