Three multidisciplinary postdoctoral scholarships (2 years)

Umeå University; Sweden; On-site; SEK 648,000 - 792,000; Full time

Benefits offered by this job

Access to interdisciplinary training activities

Tax-free fellowships

Mentorship from experts

Job summary

Umeå University is offering three two-year postdoctoral scholarships through the IceLab program, focusing on multidisciplinary projects funded by Kempestiftelserna. Each scholarship includes 720,000 SEK for salary and 75,000 SEK for research expenses, and is designed for researchers with expertise in various fields such as computational biology, mathematics, and ecology.

Candidates must hold a PhD completed within three years and show strong interest in relevant areas. Benefits include working in a collaborative environment and mentorship from experts.

Qualifications

• PhD degree or equivalent completed within three years.
• Experience or strong interest in data analytics, computational modeling, or related fields.
• Fluency in English required.

Responsibilities

• Work in a transdisciplinary team and access interdisciplinary training activities.
• Conduct research and present findings in chosen project.

Skills

Data analytics

Computational modeling

Programming

Mathematics

Statistics

Physics

Molecular biology

Microbiology

Ecology

Systems biology

Education

PhD in a relevant field

Job description

IceLab Interdisciplinary Postdoctoral Program

The Integrated Science Lab (IceLab) at Umeå University offers three two‑year postdoctoral scholarships in five multidisciplinary projects. Funding comes from Kempestiftelserna and amounts to 720,000 SEK per fellowship plus 75,000 SEK for research expenses. Fellowships are tax‑free. The start date will be between January and April 2027, and the application deadline is 17 September 2026.

Eligibility

Applicants must hold a doctoral degree or an equivalent foreign degree, completed no later than three years before the decision date. Priority is given to those who completed their PhD within that period. Candidates should have experience or strong interest in at least one of the following areas: data analytics, computational modeling, programming, mathematics, statistics, physics, molecular biology, microbiology, ecology, systems biology, or related fields. Personal qualities such as collaboration, communication, drive, critical thinking, creativity, and analytical skills are essential. Fluency in English (oral and written) is required.

Benefits

Fellowship holders will work in a transdisciplinary team, receive mentorship from complementary experts, and have access to interdisciplinary training activities. The funding covers salary, research expenses, and tax‑free scholarship.

Projects

A. Sensing the breaking point: Decoding the inputs to a cell wall integrity receptor in Chlorella vulgaris

• Build a mechanistic model coupling wall chemistry, wall stiffness, and turgor pressure to receptor activation, using transcriptomic, compositional, and microscopy data.
• Perform an identifiability analysis to identify new measurements needed.
• Design and carry out experiments that separate chemical and mechanical inputs to the receptor.
• Fit the model to experimental data and attribute receptor activation to each input.
• Predict wall failure thresholds and assess relevance to controlled cell disruption.
• Present findings at IceLab activities and network with researchers in stress‑response modelling.

Specific Qualifications for Project A PhD in computational biology, biophysics, mathematical biology, applied mathematics, or a closely related quantitative field. Experience in mechanistic or mathematical modelling of biological systems, familiarity with parameter fitting, identifiability analysis, or dynamical systems modelling is highly desirable.

Contact Laura Bacete Cano, Assistant Professor, Department of Plant Physiology, Umeå University – [e-mail]; Christiane Funk, Professor, Department of Chemistry, Umeå University – [e-mail].

B. Decoding the Hidden Logic of RNA Polymerase Allocation Under Stress

• Develop and refine models of RNA polymerase allocation among competing regulators under stress.
• Analyze time‑series gene‑expression datasets to uncover hidden sigma‑factor dynamics.
• Deepen expertise in computational physics, systems biology, and microbiology.
• Engage with IceLab seminars and interdisciplinary exchanges.
• Optionally gain experimental experience for a theory‑lab bridge.
• Strengthen independent research portfolio for a future research career.

Specific Qualifications for Project B PhD in computational biology, bioinformatics, physics, systems biology, or a related field. Strong background in mathematical or computational modeling and data analysis, with a desire to work at the interface of quantitative science and microbiology.

Contact Ludvig Lizana, Professor, Department of Physics and IceLab, Umeå University – [e-mail]; Kemal Avican, Assistant Professor, Department of Molecular Biology and IceLab, Umeå University – [e-mail].

C. Adaptive immunity as an evolutionary response to unforeseen stress

• Develop mathematical and computational models of adaptive immune evolution.
• Investigate costs and benefits of anticipatory defense systems across species.
• Explore adaptive immunity via evolutionary optimisation, complex systems theory, and control theory.
• Study divergence and loss of immune systems among lineages.
• Collaborate across disciplines: evolutionary immunology, theoretical biology, mathematics, stress‑response research.

Specific Qualifications for Project C PhD in mathematics, physics, theoretical biology, computational biology, evolutionary biology, immunology, or a related discipline. Experience in evolutionary theory, dynamical systems, stochastic modelling, control theory, or theoretical biology is desirable. Candidates should be able to communicate across biological and quantitative communities.

Contact Ryo Morimoto, Research Fellow, Department of Molecular Biology and MIMS, Umeå University – [e-mail]; Eric Libby, Associate Professor, Department of Mathematics and Mathematical Statistics and IceLab, Umeå University – [e-mail].

D. Multitrophic interaction networks as drivers of competitor coexistence under global change

• Explore indirect links across competition, facilitation, soil microbes, pollinators, and herbivores and their effect on species coexistence.
• Develop higher‑order network‑flow models for multilayer ecological networks.
• Analyse empirical datasets from Mediterranean and Arctic biomes.
• Assess whether network mechanisms hold across biomes and predict biodiversity loss under global change.
• Develop and share open‑source software for community data analysis.

Specific Qualifications for Project D PhD in ecology, physics, computational science, applied mathematics, or a related field. Strong background in community ecology and quantitative methods (statistical modelling, network analysis). Experience with plant community ecology, field experiments, plant‑soil interactions, species coexistence, or multitrophic interactions is an asset.

Contact Magnus Neuman, Staff Scientist, Department of Physics and IceLab, Umeå University – [e-mail]; Johan Olofsson, Professor, Department of Ecology, Environment and Geoscience, Umeå University – [e-mail].

E. From training load to adaptation: Modelling of stress, resilience, and performance in elite female athletes

• Explore a large‑scale longitudinal dataset of elite athletes integrating molecular, physiological, psychological, and performance variables.
• Develop predictive models of human adaptation, resilience, and failure using statistical learning, dynamical systems modelling, and high‑dimensional analyses.
• Identify early‑warning signals and tipping points in complex biological systems.
• Translate multi‑scale data into biological insight and support predictive systems‑level understanding of human performance.
• Extend project via biochemical analyses of biobanked samples when hypotheses arise.

Specific Qualifications for Project E PhD in statistics, data science, computational biology, physics, or a related quantitative discipline. Strong skills in statistical modelling, machine learning, or computational methods; experience with high‑dimensional, functional, or longitudinal data; interest in complex systems, resilience, and human biology under stress.

Contact Michael Svensson, Associate Professor, Department of Community Medicine and Rehabilitation, Umeå University – [e-mail]; Sara Sjöstedt de Luna, Professor, Department of Mathematics and Mathematical Statistics, Umeå University – [e-mail].