Randall S. Johnson

This doctoral (PhD) student position at Karolinska Institutet offers an exciting opportunity to contribute to advancing human health through deep learning modeling of cancer. The position is based in Avlant Nilsson’s research group at Karolinska Institutet and SciLifeLab, focusing on developing mechanistic AI models of cancer cells to support precision medicine. The lab integrates large-scale multi-omics data—including metabolomics, transcriptomics, and proteomics—with molecular interaction networks to construct interpretable deep learning models that capture system-level mechanisms in cancer. The central goal is to build a unified computational model describing how molecular processes such as signaling, gene regulation, and metabolism interact to determine cellular behavior. This research aims to identify biomarkers, novel drug targets, and mechanisms of resistance, ultimately enabling computer-aided design of precision therapies. The project is part of the SciLifeLab and DDLS (Data-Driven Life Science) program, providing access to a collaborative and interdisciplinary research environment and state-of-the-art computational infrastructure, including the Berzelius supercomputer. The main supervisor is Assistant Professor Avlant Nilsson, with Professor Randall S. Johnson as co-supervisor at the Department of Cell and Molecular Biology. The PhD project involves developing a unified, next-generation AI model of the cancer cell by integrating metabolism, signaling, and gene regulation into a single predictive framework. The work includes designing biologically constrained deep learning models using novel architectures based on recurrent neural networks (RNNs) to capture how molecular states evolve and give rise to cellular phenotypes. Key components of the project are model development, in silico target discovery, and biomarker discovery. As a doctoral student, you will work closely with the computational team and collaborators, contributing to experiment design guided by model predictions. Karolinska Institutet offers a creative and inspiring environment, a wide range of elective courses, and opportunities for international exchanges. The doctoral studentship provides a contractual monthly salary for up to four years full-time, with additional benefits such as free access to the modern gym and medical care reimbursements. Eligibility requirements include a second-cycle/advanced/master qualification or equivalent, or completion of at least 240 credits (with at least 60 at the advanced/master level), or substantially equivalent knowledge. Proficiency in English equivalent to English B/English 6 at Swedish upper secondary school is required. Necessary skills include strong programming abilities in Python, familiarity with version control (e.g. Git), and the ability to work independently and collaboratively. Desirable qualifications are a background in physics, computational biology, machine learning, applied mathematics, experience with deep learning frameworks (e.g. PyTorch, TensorFlow), familiarity with cancer and cell biology, high-dimensional or dynamical systems, and experience analyzing biological or multi-omics data. Applicants from quantitative disciplines are encouraged, and prior biological expertise is not required. The application process is managed through the Varbi recruitment system. Applicants should submit a personal letter, CV, degree projects and previous publications, documentation of skills and personal qualities, and documents certifying eligibility. Selection is based on documented subject knowledge, analytical skill, and other relevant experience. All applicants will be informed when recruitment is completed. The deadline for applications is May 27, 2026. Join Karolinska Institutet and contribute to better health for all through innovative research in cancer modeling and precision medicine.

This PhD position at Karolinska Institutet and SciLifeLab offers an exciting opportunity to contribute to advancing human health through cutting-edge research in cancer biology and precision medicine. The project is based in Avlant Nilsson’s research group, which focuses on developing mechanistic AI models of cancer cells by integrating large-scale multi-omics data (metabolomics, transcriptomics, proteomics) with molecular interaction networks. The central aim is to build interpretable deep learning models that capture system-level mechanisms in cancer, ultimately enabling computer-aided design of precision therapies. The lab’s goal is to construct a unified computational model describing how molecular processes such as signaling, gene regulation, and metabolism interact to determine cellular behavior. This research is part of the SciLifeLab and DDLS (Data-Driven Life Science) program, providing access to a highly collaborative and interdisciplinary environment, as well as state-of-the-art computational infrastructure including the Berzelius supercomputer. The main supervisor is Assistant Professor Avlant Nilsson, with Professor Randall S. Johnson as co-supervisor. The project involves developing next-generation AI models of cancer cells by integrating metabolism, signaling, and gene regulation into a single predictive framework. You will design biologically constrained deep learning models using novel architectures based on recurrent neural networks (RNNs) to capture how molecular states evolve and give rise to cellular phenotypes. Key components of the project include: Model development: Integrating modules of cellular subsystems to predict cellular states In silico target discovery: Predicting metabolic vulnerabilities and prioritizing candidates for experimental validation Biomarker discovery: Identifying molecular states associated with drug response across cancer contexts You will collaborate closely with computational team members and external partners, contributing to experiment design guided by model predictions. The research environment is creative and inspiring, with opportunities for international exchanges and access to a wide range of elective courses. Eligibility requirements include a master’s degree or equivalent, proficiency in English (English B/English 6 at Swedish upper secondary school), and strong programming skills in Python. Familiarity with version control (e.g. Git), the ability to work independently and collaboratively, and proficiency in written and spoken English are necessary. Desirable qualifications include backgrounds in physics, computational biology, machine learning, applied mathematics, experience with deep learning frameworks (PyTorch, TensorFlow), familiarity with cancer and cell biology, and experience analyzing biological or multi-omics data. Applicants from quantitative disciplines are encouraged, and prior biological expertise is not required. The doctoral student will be employed on a full-time studentship for up to four years, receiving a contractual monthly salary. Additional benefits include free access to the university gym and medical care reimbursements. Karolinska Institutet values diversity and inclusion, fostering an environment where all individuals are empowered to contribute their unique perspectives. To apply, submit your application and supporting documents through the Varbi recruitment system. Required documents include a personal letter, CV, degree projects, previous publications, documentation of desirable skills, and certificates of eligibility. The deadline for applications is 27 May 2026. Selection will be based on subject knowledge, analytical skill, and other relevant experience. All applicants will be informed when the recruitment is completed. Join Karolinska Institutet and make a difference in the fight against cancer!

Karolinska Institutet, one of the world's leading medical universities, is offering a PhD position in deep learning modeling of cancer. The position is based in Avlant Nilsson's research group at Karolinska Institutet and SciLifeLab, focusing on the development of mechanistic AI models of cancer cells to advance precision medicine. The group integrates large-scale multi-omics datasets (such as metabolomics, transcriptomics, and proteomics) with molecular interaction networks to build interpretable deep learning models that describe cancer mechanisms at the systems level. The overarching goal is to create a unified model explaining how molecular processes like signaling, gene regulation, and metabolism interact to determine cellular behavior, aiming to identify biomarkers, new drug targets, and resistance mechanisms, and ultimately enable computer-aided design of precision treatments. The project is part of the SciLifeLab and DDLS (Data-Driven Life Science) program, providing access to a highly collaborative and interdisciplinary research environment and advanced computational infrastructure, including the Berzelius supercomputer. The main supervisor is Assistant Professor Avlant Nilsson, with Professor Randall S. Johnson as co-supervisor at the Department of Cell and Molecular Biology. The doctoral project addresses the dynamic nature of cell metabolism in cancer, a hallmark of tumor development. Traditionally, metabolism, signaling, and gene regulation have been studied with separate modeling frameworks, limiting our understanding of their interplay and the prediction of metabolic effects of drugs targeting cellular signaling. In this PhD project, you will develop a next-generation AI model of the cancer cell by integrating metabolism, signaling, and gene regulation into a unified predictive framework. The work involves developing biologically constrained deep learning models based on a novel architecture with recurrent neural networks (RNNs) to describe how molecular states evolve over time and give rise to cellular phenotypes. The project consists of three main parts: (1) Model development—integrating modules for cellular subsystems to predict cellular states; (2) Target identification in silico—predicting metabolic vulnerabilities and prioritizing candidates for experimental validation; (3) Biomarker identification—identifying molecular states linked to drug response in various cancer contexts. You will work closely with the computational research group and collaborators, contributing to the design of experiments based on model predictions. Karolinska Institutet offers a creative, international, and inspiring environment filled with expertise and curiosity. As a PhD student, you will have an individual research project, a skilled supervisor, a wide range of elective courses, and the opportunity to work in a successful research group. KI collaborates with leading universities worldwide, offering opportunities for international exchanges. As an employed doctoral student, you receive a contractual salary, free access to modern fitness facilities, and compensation for medical care. Eligibility requirements include a master's degree or equivalent, or at least 240 ECTS credits with 60 at the advanced level, or equivalent knowledge. English proficiency equivalent to English B/English 6 is required. Strong programming skills (e.g., Python), experience with version control (e.g., Git), ability to work independently and in teams, and good communication skills in English are necessary. Merits include background in physics, computational biology, machine learning, applied mathematics, experience with deep learning frameworks (e.g., PyTorch, TensorFlow), knowledge in cancer and cell biology, experience with high-dimensional or dynamic systems, and analysis of biological or multi-omics data. Prior biology experience is not required; applicants from quantitative disciplines are encouraged. The group values diversity and inclusion, fostering an environment where all individuals are empowered to contribute their unique perspectives and skills toward the shared goal of advancing precision medicine in cancer. The doctoral position is a full-time employment for up to 4 years. The application deadline is May 27, 2026. Apply via the Varbi recruitment tool, submitting your application in English or Swedish, including a personal letter, CV, any theses and publications, documents verifying required skills and qualifications, and proof of eligibility. Follow instructions on the doctoral education eligibility web pages. All applicants will be informed when the recruitment is completed.