Universität Basel
Postdoctoral Researcher in Confidential High Performance Computing for AI in Cancer Care
Basel
100%
Start date: September 2026 (flexible) Duration: 3 years (36 months) Project The High Performance Computing (HPC) research group (Prof. Florina M. Ciorba), Department of Mathematics and Computer Science, University of Bas…
Details
Start date: September 2026 (flexible)
Duration: 3 years (36 months)
Project
The High Performance Computing (HPC) research group (Prof. Florina M. Ciorba), Department of Mathematics and Computer Science, University of Basel, invites applications for a Postdoctoral Researcher funded by the SNF Bridge Discovery project "Family Gene Toolkit (FGT)" (grant no. 237643), a digital platform supporting families affected by hereditary cancer syndromes (HBOC, Lynch Syndrome). The next-generation FGT v3.0 will integrate large language models, multilingual AI, and confidential HPC to support patients and clinicians in real-world healthcare environments.
Your position
You will lead research on trustworthy and aligned LLMs operating in confidential HPC environments, involving training and adapting 70B-scale multilingual and medical language models on secure HPC infrastructure. Your work focuses on three areas:
Preference optimization and LLM alignment: design preference-based training and fine-tuning methods (RLHF, PPO, DPO, reward modeling) for medical and multilingual LLMs.
Agentic and tool-augmented AI systems: develop reasoning and interaction capabilities including RAG, in-context learning, chain-of-thought reasoning, and agentic workflows with external tools.
Trustworthy AI evaluation and confidential deployment: develop evaluation pipelines (e.g., LLM-as-a-judge), robustness/bias/hallucination mitigation, and multilingual evaluation protocols.
As part of this position, your tasks also include:
Conduct high-quality research aligned with the project objectives
Publish in top venues (NeurIPS, ICML, ICLR, ACL/EMNLP, MLSys, SC, HPDC, EuroSys, ASPLOS, etc.)
Present research at international conferences, workshops, and seminars
Contribute to open-source software and reproducible research artifacts
Collaborate actively, fruitfully, and respectfully with the PIs, the HPC group, and partner institutions
Contribute to teaching (. one class per semester) and co-supervise Bachelor's/Master's students
Your profile
We seek a highly motivated researcher interested in integrating foundational AI and HPC research with real-world healthcare applications.
Required
PhD in Computer Science / AI / Machine Learning
Strong publication record in AI, ML systems, or related areas
Strong programming skills in , C/C++ and experience with PyTorch, TensorFlow, JAX, or similar ML frameworks
Experience with large-scale training or inference of LLMs
Interest in LLM alignment, reinforcement learning, or generative AI systems
Fluency in English; clear communication, problem-solving, and collaborative mindset
Highly desirable
Experience with distributed training or ML systems
Knowledge of privacy-enhancing technologies and parallel programming
Experience with multilingual AI or agentic systems
Diversity: We welcome applications from candidates with diverse backgrounds. Even if you do not meet all requirements, we encourage you to apply if you identify with the profile.
We offer you
Competitive 100% funding per SNSF guidelines (~CHF 90'000/year)
Access to modern GPU clusters and confidential-computing infrastructure
Collaboration with leading researchers in AI & HPC systems and digital health
Support for international conference travel and networking
Stimulating research environment at a leading European university
Application / Contact
Interested?
Submit a single PDF file (named ) and a motivation statement via the application portal containing:
Curriculum vitae with publication list (open-access links)
Degree Transcripts (Bachelor's, Master's, Doctoral)
Theses (Bachelor's, Master's, and Doctoral)
Links to own code repositories or software projects
At least one relevant publication with a short justification
Motivation statement addressing the questions: (1) why this position, (2) which aspects interest you, (3) relevant prior experience, and (4) goals during the postdoc
Contact details for 1-2 referees (recommendation letters will be discarded)
Application deadline: April 24, 2026 (rolling review until filled). Only short-listed candidates will be invited for interview. Due to the anticipated volume of applications, notifications are sent only to short-listed applicants.
Applications by e-mail will not be considered. For recruiters/staffing agencies: Acquisition in response to this advertisement is not appreciated.
For questions: Email Prof. Florina M. Ciorba (Write an email) with subject: "BRIDGE FGT - Postdoc position"
Universität Basel
4000 Basel
Postdoctoral Researcher in Confidential High Performance Computing for AI in Cancer Care (100%)
Start date: September 2026 (flexible)
Duration: 3 years (36 months)
Project
The High Performance Computing (HPC) research group (Prof. Florina M. Ciorba), Department of Mathematics and Computer Science, University of Basel, invites applications for a Postdoctoral Researcher funded by the SNF Bridge Discovery project "Family Gene Toolkit (FGT)" (grant no. 237643), a digital platform supporting families affected by hereditary cancer syndromes (HBOC, Lynch Syndrome). The next-generation FGT v3.0 will integrate large language models, multilingual AI, and confidential HPC to support patients and clinicians in real-world healthcare environments.
Your position
You will lead research on trustworthy and aligned LLMs operating in confidential HPC environments, involving training and adapting 70B-scale multilingual and medical language models on secure HPC infrastructure. Your work focuses on three areas:
Preference optimization and LLM alignment: design preference-based training and fine-tuning methods (RLHF, PPO, DPO, reward modeling) for medical and multilingual LLMs.
Agentic and tool-augmented AI systems: develop reasoning and interaction capabilities including RAG, in-context learning, chain-of-thought reasoning, and agentic workflows with external tools.
Trustworthy AI evaluation and confidential deployment: develop evaluation pipelines (e.g., LLM-as-a-judge), robustness/bias/hallucination mitigation, and multilingual evaluation protocols.
As part of this position, your tasks also include:
Conduct high-quality research aligned with the project objectives
Publish in top venues (NeurIPS, ICML, ICLR, ACL/EMNLP, MLSys, SC, HPDC, EuroSys, ASPLOS, etc.)
Present research at international conferences, workshops, and seminars
Contribute to open-source software and reproducible research artifacts
Collaborate actively, fruitfully, and respectfully with the PIs, the HPC group, and partner institutions
Contribute to teaching (. one class per semester) and co-supervise Bachelor's/Master's students
Your profile
We seek a highly motivated researcher interested in integrating foundational AI and HPC research with real-world healthcare applications.
Required
PhD in Computer Science / AI / Machine Learning
Strong publication record in AI, ML systems, or related areas
Strong programming skills in , C/C++ and experience with PyTorch, TensorFlow, JAX, or similar ML frameworks
Experience with large-scale training or inference of LLMs
Interest in LLM alignment, reinforcement learning, or generative AI systems
Fluency in English; clear communication, problem-solving, and collaborative mindset
Highly desirable
Experience with distributed training or ML systems
Knowledge of privacy-enhancing technologies and parallel programming
Experience with multilingual AI or agentic systems
Diversity: We welcome applications from candidates with diverse backgrounds. Even if you do not meet all requirements, we encourage you to apply if you identify with the profile.
We offer you
Competitive 100% funding per SNSF guidelines (~CHF 90'000/year)
Access to modern GPU clusters and confidential-computing infrastructure
Collaboration with leading researchers in AI & HPC systems and digital health
Support for international conference travel and networking
Stimulating research environment at a leading European university
Application / Contact
Interested?
Submit a single PDF file (named ) and a motivation statement via the application portal containing:
Curriculum vitae with publication list (open-access links)
Degree Transcripts (Bachelor's, Master's, Doctoral)
Theses (Bachelor's, Master's, and Doctoral)
Links to own code repositories or software projects
At least one relevant publication with a short justification
Motivation statement addressing the questions: (1) why this position, (2) which aspects interest you, (3) relevant prior experience, and (4) goals during the postdoc
Contact details for 1-2 referees (recommendation letters will be discarded)
Application deadline: April 24, 2026 (rolling review until filled). Only short-listed candidates will be invited for interview. Due to the anticipated volume of applications, notifications are sent only to short-listed applicants.
Applications by e-mail will not be considered. For recruiters/staffing agencies: Acquisition in response to this advertisement is not appreciated.
For questions: Email Prof. Florina M. Ciorba (Write an email) with subject: "BRIDGE FGT - Postdoc position"
Universität Basel
4000 Basel jid7b2b8aesy jit0415sy jiy26sy
Duration: 3 years (36 months)
Project
The High Performance Computing (HPC) research group (Prof. Florina M. Ciorba), Department of Mathematics and Computer Science, University of Basel, invites applications for a Postdoctoral Researcher funded by the SNF Bridge Discovery project "Family Gene Toolkit (FGT)" (grant no. 237643), a digital platform supporting families affected by hereditary cancer syndromes (HBOC, Lynch Syndrome). The next-generation FGT v3.0 will integrate large language models, multilingual AI, and confidential HPC to support patients and clinicians in real-world healthcare environments.
Your position
You will lead research on trustworthy and aligned LLMs operating in confidential HPC environments, involving training and adapting 70B-scale multilingual and medical language models on secure HPC infrastructure. Your work focuses on three areas:
Preference optimization and LLM alignment: design preference-based training and fine-tuning methods (RLHF, PPO, DPO, reward modeling) for medical and multilingual LLMs.
Agentic and tool-augmented AI systems: develop reasoning and interaction capabilities including RAG, in-context learning, chain-of-thought reasoning, and agentic workflows with external tools.
Trustworthy AI evaluation and confidential deployment: develop evaluation pipelines (e.g., LLM-as-a-judge), robustness/bias/hallucination mitigation, and multilingual evaluation protocols.
As part of this position, your tasks also include:
Conduct high-quality research aligned with the project objectives
Publish in top venues (NeurIPS, ICML, ICLR, ACL/EMNLP, MLSys, SC, HPDC, EuroSys, ASPLOS, etc.)
Present research at international conferences, workshops, and seminars
Contribute to open-source software and reproducible research artifacts
Collaborate actively, fruitfully, and respectfully with the PIs, the HPC group, and partner institutions
Contribute to teaching (. one class per semester) and co-supervise Bachelor's/Master's students
Your profile
We seek a highly motivated researcher interested in integrating foundational AI and HPC research with real-world healthcare applications.
Required
PhD in Computer Science / AI / Machine Learning
Strong publication record in AI, ML systems, or related areas
Strong programming skills in , C/C++ and experience with PyTorch, TensorFlow, JAX, or similar ML frameworks
Experience with large-scale training or inference of LLMs
Interest in LLM alignment, reinforcement learning, or generative AI systems
Fluency in English; clear communication, problem-solving, and collaborative mindset
Highly desirable
Experience with distributed training or ML systems
Knowledge of privacy-enhancing technologies and parallel programming
Experience with multilingual AI or agentic systems
Diversity: We welcome applications from candidates with diverse backgrounds. Even if you do not meet all requirements, we encourage you to apply if you identify with the profile.
We offer you
Competitive 100% funding per SNSF guidelines (~CHF 90'000/year)
Access to modern GPU clusters and confidential-computing infrastructure
Collaboration with leading researchers in AI & HPC systems and digital health
Support for international conference travel and networking
Stimulating research environment at a leading European university
Application / Contact
Interested?
Submit a single PDF file (named ) and a motivation statement via the application portal containing:
Curriculum vitae with publication list (open-access links)
Degree Transcripts (Bachelor's, Master's, Doctoral)
Theses (Bachelor's, Master's, and Doctoral)
Links to own code repositories or software projects
At least one relevant publication with a short justification
Motivation statement addressing the questions: (1) why this position, (2) which aspects interest you, (3) relevant prior experience, and (4) goals during the postdoc
Contact details for 1-2 referees (recommendation letters will be discarded)
Application deadline: April 24, 2026 (rolling review until filled). Only short-listed candidates will be invited for interview. Due to the anticipated volume of applications, notifications are sent only to short-listed applicants.
Applications by e-mail will not be considered. For recruiters/staffing agencies: Acquisition in response to this advertisement is not appreciated.
For questions: Email Prof. Florina M. Ciorba (Write an email) with subject: "BRIDGE FGT - Postdoc position"
Universität Basel
4000 Basel
Postdoctoral Researcher in Confidential High Performance Computing for AI in Cancer Care (100%)
Start date: September 2026 (flexible)
Duration: 3 years (36 months)
Project
The High Performance Computing (HPC) research group (Prof. Florina M. Ciorba), Department of Mathematics and Computer Science, University of Basel, invites applications for a Postdoctoral Researcher funded by the SNF Bridge Discovery project "Family Gene Toolkit (FGT)" (grant no. 237643), a digital platform supporting families affected by hereditary cancer syndromes (HBOC, Lynch Syndrome). The next-generation FGT v3.0 will integrate large language models, multilingual AI, and confidential HPC to support patients and clinicians in real-world healthcare environments.
Your position
You will lead research on trustworthy and aligned LLMs operating in confidential HPC environments, involving training and adapting 70B-scale multilingual and medical language models on secure HPC infrastructure. Your work focuses on three areas:
Preference optimization and LLM alignment: design preference-based training and fine-tuning methods (RLHF, PPO, DPO, reward modeling) for medical and multilingual LLMs.
Agentic and tool-augmented AI systems: develop reasoning and interaction capabilities including RAG, in-context learning, chain-of-thought reasoning, and agentic workflows with external tools.
Trustworthy AI evaluation and confidential deployment: develop evaluation pipelines (e.g., LLM-as-a-judge), robustness/bias/hallucination mitigation, and multilingual evaluation protocols.
As part of this position, your tasks also include:
Conduct high-quality research aligned with the project objectives
Publish in top venues (NeurIPS, ICML, ICLR, ACL/EMNLP, MLSys, SC, HPDC, EuroSys, ASPLOS, etc.)
Present research at international conferences, workshops, and seminars
Contribute to open-source software and reproducible research artifacts
Collaborate actively, fruitfully, and respectfully with the PIs, the HPC group, and partner institutions
Contribute to teaching (. one class per semester) and co-supervise Bachelor's/Master's students
Your profile
We seek a highly motivated researcher interested in integrating foundational AI and HPC research with real-world healthcare applications.
Required
PhD in Computer Science / AI / Machine Learning
Strong publication record in AI, ML systems, or related areas
Strong programming skills in , C/C++ and experience with PyTorch, TensorFlow, JAX, or similar ML frameworks
Experience with large-scale training or inference of LLMs
Interest in LLM alignment, reinforcement learning, or generative AI systems
Fluency in English; clear communication, problem-solving, and collaborative mindset
Highly desirable
Experience with distributed training or ML systems
Knowledge of privacy-enhancing technologies and parallel programming
Experience with multilingual AI or agentic systems
Diversity: We welcome applications from candidates with diverse backgrounds. Even if you do not meet all requirements, we encourage you to apply if you identify with the profile.
We offer you
Competitive 100% funding per SNSF guidelines (~CHF 90'000/year)
Access to modern GPU clusters and confidential-computing infrastructure
Collaboration with leading researchers in AI & HPC systems and digital health
Support for international conference travel and networking
Stimulating research environment at a leading European university
Application / Contact
Interested?
Submit a single PDF file (named ) and a motivation statement via the application portal containing:
Curriculum vitae with publication list (open-access links)
Degree Transcripts (Bachelor's, Master's, Doctoral)
Theses (Bachelor's, Master's, and Doctoral)
Links to own code repositories or software projects
At least one relevant publication with a short justification
Motivation statement addressing the questions: (1) why this position, (2) which aspects interest you, (3) relevant prior experience, and (4) goals during the postdoc
Contact details for 1-2 referees (recommendation letters will be discarded)
Application deadline: April 24, 2026 (rolling review until filled). Only short-listed candidates will be invited for interview. Due to the anticipated volume of applications, notifications are sent only to short-listed applicants.
Applications by e-mail will not be considered. For recruiters/staffing agencies: Acquisition in response to this advertisement is not appreciated.
For questions: Email Prof. Florina M. Ciorba (Write an email) with subject: "BRIDGE FGT - Postdoc position"
Universität Basel
4000 Basel jid7b2b8aesy jit0415sy jiy26sy
