Project assistant in Applied Biochemistry

Description of the workplace 

The Saragovi Lab is part of the Division of Applied Biochemistry at the Department of Chemistry. Our group focuses on developing and implementing deep learning, reinforcement learning, and other AI-based protein design pipelines to generate protein assemblies that guide the formation of inorganic materials, including semiconductors, magnetic materials, and other functional phases. More broadly, we aim to model the transfer of structural information in a hierarchical manner from proteins to non-biological materials, extending the concept of the central dogma into new domains of bio-inorganic information flow. The position is placed within a highly interdisciplinary environment that links the Division of Applied Biochemistry with research teams at the Center for Molecular Protein Science (CMPS) and semiconductor physics groups within NanoLund. Our workplace currently consists of several master’s students and research interns and will grow with new PhD students and postdocs in early 2026. The group is physically located at Kemicentrum (KC) in Lund and embedded in the broader NanoLund ecosystem, which connects more than 400 researchers across materials science, chemistry, and physics.

New team members will join a collaborative setting with complementary expertise in protein design, biochemistry,  and inorganic phase characterization. The specific role fits into an expanding design–build–test pipeline where computational modelling and experimental validation are tightly integrated. The workplace strives for an inclusive, supportive, and creative environment, where early-career researchers are encouraged to contribute ideas, develop independence, and participate in shaping new research directions. The lab has access to extensive computational resources (GPU clusters), high-throughput instruments (FACS, mass photometry, ITC, SPR, and others), and state-of-the-art semiconductor characterization facilities such as high-resolution TEM, ellipsometry, and clean-room processing through NanoLund. More information can be found at: https://saragovi.science and https://www.nano.lu.se.

 We offer 

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme. We offer an opportunity to develop a strong foundation in modern protein design within the rapidly emerging field of hierarchical material engineering. The position provides hands-on experience with AI-driven protein design pipelines, experimental workflows for protein–inorganic assembly, and access to cutting-edge infrastructure at Kemicentrum and NanoLund. For motivated candidates, the role can also serve as a valuable transition period toward a future PhD position in the group or within the broader Lund University environment. Read more on the University website about being a Lund University employee Work at Lund University 

Work duties 

The work duties include developing and integrating computational and experimental tools for the design of hierarchical protein–inorganic materials. You will work at the interface of AI-driven protein design, high throughput biochemistry, and materials characterization.

You will have particular responsibility for:

• Adjusting and optimizing reinforcement-learning models and symmetric placement design scripts to support new design pipelines for hierarchical protein–inorganic systems.
• Developing and applying computational pipelines for the de novo design of conducting twisted nanowires and related protein–metal architectures.
• Establishing and running high-throughput biochemical pipelines for screening, validating, and characterizing designed protein assemblies.
• Performing experimental workflows including protein expression, purification, biophysical assays, and analysis of protein–inorganic templating.
• Collaborating with partner groups for advanced structural characterization (e.g., TEM, cryo-EM, spectroscopy) and functional characterization (e.g., electrical, optical, or magnetic measurements).
• Documenting and communicating results, and contributing to the lab’s iterative design–build–test cycle.

Qualifications  

Required qualifications for the position are:

•  You have a master’s degree in biotechnology, molecular biology, computational biology, or a closely related field.
• You have experience with computational protein design pipelines, including practical use of RFdiffusion and ProteinMPNN.
  
  
• You have substantial coding experience in Python and can work comfortably in a Linux environment.
  
  
• You have hands-on laboratory experience in high throughput protein expression and purification.
  
  
• You have the ability to work independently, plan experiments, and document your results clearly.
  
  
• You have good collaborative skills and can contribute effectively to interdisciplinary teamwork.
  
  
• You have good communication skills in English, both written and spoken.

Additional qualifications for the position are:

•  It is an additional qualification if you have experience with high-throughput biochemistry, including 96- or 384-well plate expression, growth, and purification pipelines.
• It is an additional qualification if you have experience developing or maintaining AI-based protein design workflows.
  
  
• It is an advantage if you have experience running molecular dynamics (MD) simulations, for example using GROMACS or transition path sampling (TPS) to analyze structural changes over time.
  
  
• Experience in contributing to collaborative research is a plus.
  
  
• Additional relevant coursework, training, or certifications in protein engineering, machine learning, or computational chemistry are considered merits.

Consideration will also be given to how the applicant’s experience and skills complement and strengthen ongoing research within the department, and how they stand to contribute to its future development. 

Further information 

The position is a full-time fixed-term appointment beginning on 1 February 2026 and continuing until 1 June 2026, or by agreement, but not beyond this date.

How to apply 

Applications are to be submitted via the University’s recruitment system. The application should include a CV and a personal letter justifying your interest in the position and how it matches your qualifications. The application should also include a degree certificates or equivalent and any other document to which you would like to draw attention (copies of grade transcripts, details of referees, letters of recommendation, etc.)

Welcome with your application!