Postdoctoral Fellow in Evolutionary Prediction

The Department of Zoology is one of the departments within the Faculty of Science and has approximately 80 employees including researchers, PhD students, and administrative staff. Research and education at the department occurs in an international environment and is focused on animal biology. Outstanding and high impact research is conducted in a variety of fields, including evolutionary biology, molecular and population genetics, genomics, conservation, and behavior. More information about us, please visit: the Department of Zoology.

SciLifeLab is a national research infrastructure. In Stockholm, SciLifeLab is located on Campus Solna, where research groups from Stockholm University, the Royal Institute of Technology, and the Karolinska Institute conduct internationally outstanding research in the life sciences.

Project description

The position will be associated with Assistant Professor Lisandro Milocco’s research group located at SciLifeLab (Solna) and the Department of Zoology, Stockholm University. The research group is part of the National Program for Data-driven Life Science (DDLS), generously funded by the Knut and Alice Wallenberg Foundation: www.scilifelab.se/data-driven/

Our group focuses on studying different aspects of evolvability – the ability of organisms to evolve. We are interested in developing computational and mathematical tools to understand and quantify evolvability while exploring its potential applications. In particular, we focus on evolutionary prediction: can we use a deeper understanding of evolvability to predict and, potentially, control evolutionary processes? Read more about our approaches and research themes here: www.scilifelab.se/researchers/lisandro-milocco/

This project leverages the rise of data-driven dynamic modeling—from fluid dynamics to ecosystem studies—to uncover the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past and future developmental timepoints from time-series data. Our recent theoretical work suggests that these learned relationships can generalize across conditions, and we will test this using both simulated and available experimental datasets. This role offers a unique opportunity to translate transformative theoretical insights into a practical framework for understanding how new phenotypic variation arises.

We are looking for up to two highly motivated postdoctoral fellows to work on different aspects of this interdisciplinary project, which combines evolutionary-developmental biology, dynamical systems, and machine learning.

While the initial focus of the position is on this project, we offer significant opportunity for the applicant to develop their own independent research trajectory in alignment with the group’s broader scientific vision. We are thus especially interested in candidates who demonstrate strong independence and a proactive approach to exploring innovative research directions.

Keywords:  systems biology, developmental biology, dynamical systems, evolution, nonlinear dynamics, biophysics, machine learning, evo-devo 

Main responsibilities

The successful applicant will lead the development and validation of data-driven dynamic models using time-series data of developmental trajectories. The main tasks include:

1. Develop the data-driven framework required to model developmental rules. The applicant will implement and evaluate various machine learning algorithms to determine the most efficient approach in terms of prediction accuracy and data requirements.
2. Test the algorithm using simulated data. For this, the applicant will generate in silico datasets from diverse computational models of development, such as models of tooth development and gene regulatory networks. They will leverage the flexibility of simulated data to systematically explore factors like measurement noise, sampling frequency, and time delays, thereby defining the empirical data requirements for the framework.
3. Apply the framework to readily available experimental data and perform a metanalysis.

In addition, we expect the successful applicant to share their expertise with other members of the group, collaborate actively with team members, present research findings in national and international conferences, and prepare manuscripts for publication.

Qualification requirements

Postdoctoral positions are appointed primarily for purposes of research. Applicants are expected to hold a Swedish doctoral degree or an equivalent degree from another country.

Assessment criteria

The degree must have been completed at latest before the employment decision is made, but no more than three years before the closing date. An older degree may be acceptable under special circumstances. Special reasons refer to sick leave, parental leave, elected positions in trade unions, service in the total defense, or other similar circumstances as well as clinical attachment or service/assignments relevant to the subject area.

The doctoral degree should be in relevant fields of biology (e.g., systems, evolutionary, developmental, computational), physics, mathematics or related fields.

In the appointment process, special attention will be given to research skills. Expertise and deep interest in the following three areas will be considered an advantage for the position:

• Evolutionary and developmental biology: understanding of concepts like developmental bias, developmental constraints and evolvability; knowledge of methods for automated phenotyping during development (e.g., for gene expression and morphology).
• Dynamical systems: knowledge of linear and nonlinear dynamical systems theory, including differential equations, simulation techniques, state-space and input-output representations, time-delay embedding, and/or time series analysis from experimental data.
• Machine learning: experience with algorithms such as nearest-neighbor, simplex projection, recurrent neural networks, singular value decomposition and/or autoencoders; experience in frameworks like TensorFlow or PyTorch.

The selection among the eligible candidates will be based on the following criteria:

• The applicant’s documented knowledge and ability to perform high quality research within the described area (PhD thesis, publication record).
• Capacity for analytical and creative thinking.
• Enthusiasm, dedication and an ability to work both independently and in a team.
• Drive and independence.
• Written and oral proficiency in English.

About the employment

The position involves full-time employment for a minimum of two years and a maximum of three years, with the possibility of extension under special circumstances. Start date as per agreement.

We offer

• A professionally stimulating, interdisciplinary, and diverse work environment. Flexibility for the applicant to develop their own interests, with guidance and support from both the advisor and the department.
• Opportunities for a research stay in a collaborators' home University, as well as support for attendance at national and international conferences.
• Joining the Data-Driven Life Sciences initiative – one of Sweden’s largest and most ambitious research initiatives, focusing on using data, computational methods, and artificial intelligence to study biological systems, offering ample opportunity for networking and collaboration.
• Access to advanced computing facilities via the Swedish National Supercomputer Centre and SciLifeLab.
• Attractive welfare benefits and the advantage of Stockholm’s family-friendly surrounding.

Stockholm University strives to be a workplace free from discrimination and with equal opportunities for all.

Contact

Further information about the position can be obtained from Asst. Prof. Lisandro Milocco , lisandro.milocco@zoologi.su.se.

Application

Apply for the position at Stockholm University's recruitment system. Attach a personal letter describing past research and future research interests and goals (max. 2 pages) and CV as well as the attachments requested in the application form. We highly recommend reading relevant literature to prepare your application. E.g., https://doi.org/10.1073/pnas.2320413121, https://doi.org/10.1111/ede.12449 and https://doi.org/10.1073/pnas.2117916119.

It is the responsibility of the applicant to ensure that the application is complete in accordance with the instructions in the job advertisement, and that it is submitted before the deadline.

The instructions for applicants are available at: How to apply for a position.