Post

PhD Position in Ecological Complexity Focusing on Memory Formation in Ecological Systems

The Computational Science Lab (CSL) at the Informatics Institute at UvA, is seeking a motivated PhD candidate to join our team focusing on modeling memory formation in ecological systems. The successful candidate will be responsible for constructing, simulating, and validating computational models of empirical data based on ecological theory.

The primary goal of the PhD project is to understand how memory affects information processing and resilience across ecological systems. Ecological communities are highly connected networks interacting in various ways transferring energy, information and biomass. These structures are primarily shaped by interactions that depend on both the behavioural and physiological responses of individual species to their environment as well as on historical contingencies. The latter process is the so-called ecological memory, which is the result of past environmental conditions on the current structure of species interactions in ecological communities. This concept can be applied to other complex systems in other fields such as computational social science. To accomplish this goal, computational models must be developed to account for the impact of past events and then tested against empirical data. These tools may include neural models, individual based models or population dynamic models.

The successful candidate will have the opportunity to work in a dynamic and interdisciplinary research team, collaborating with researchers from various disciplines, including ecology, microbiology, computational modelling to ensure that models are relevant, useful for practical applications and have societal impact. Excellent English communication skills, both written and spoken, are necessary.

What are you going to do?

As a PhD candidate you will:

  • Engage in advanced research on computational modelling techniques and their applications in ecology.
  • Develop and implement computational methods to analyse and detect memory in ecological time series.
  • Collaborate with researchers from other fields such as microbiology, complex systems, statistics and computational science.
  • Contribute to academic publications and present your findings at international conferences and seminars.
  • Actively participate in the department’s educational programs, possibly including the supervision of undergraduate or master’s level students.

What do you have to offer?

We are looking for an enthusiastic and driven candidate who meets the following requirements:

  • Educational Background: You should have a Master’s degree in computational science, complex systems, physics, computational biology or similar quantitative field.
  • Experience: Experience in working with dynamical models, stochastic processes and statistical analysis is required, as, e.g., proven by a Master’s thesis in this field.
  • Technical Proficiency: Expertise in programming using Python, Julia or R is required, and hands-on experience with modelling approaches is highly desirable.
  • Motivation: A strong motivation to apply computational modelling to ecological data is essential.
  • Analytical and Problem-Solving Skills: You should have excellent analytical abilities and a problem-solving mindset, with the capacity to work on theoretical models and datasets.
  • Communication Skills: Strong written and verbal communication skills are required. You should be able to articulate your research findings effectively to both academic and non-academic audiences. Proficiency in English is required.
  • Teamwork and Independence: You should be highly motivated, committed to your research, and have a passion for academic excellence. While you should be able to work independently, you must also be a good team player, ready to collaborate with other researchers and contribute to joint projects.

Not required, but helpful:

  • Previous Experience: Any prior research experience in ecological modelling will be highly regarded.

Our offer

A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). The preferred starting date is to be discussed. This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.

The gross monthly salary, based on 38 hours per week and dependent on relevant experience, ranges between € 2,872 to € 3,670 (scale P). UvA additionally offers an extensive package of secondary benefits, including 8% holiday allowance and a year-end bonus of 8.3%. The UFO profile PhD Candidate is applicable. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The Collective Labour Agreement of Dutch UniversitiesUniversiteiten van Nederland is applicable.

Besides the salary and a vibrant and challenging environment at Science Park, we offer you multiple fringe benefits:

  • 232 holiday hours per year (based on fulltime) and extra holidays between Christmas and 1 January;
  • Multiple courses to follow from our Teaching and Learning Centre;
  • A complete educational program for PhD students
  • Multiple courses on topics such as leadership for academic staff;
  • Multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses;
  • 7 weeks birth leave (partner leave) with 100% salary;
  • Partly paid parental leave;
  • The possibility to set up a workplace at home;
  • A pension at ABP for which UvA pays two third part of the contribution;
  • The possibility to follow courses to learn Dutch;
  • Help with housing for a studio or small apartment when you’re moving from abroad.

Source and more details: https://vacatures.uva.nl/UvA/job/PhD-Position-in-Ecological-Complexity-Focusing-on-Memory-Formation-in-Ecological-Systems/804547902/

Desktop View

This post is licensed under CC BY 4.0 by the author.