Assistant Professor; Adiunkt Department of Hydrology and Hydrodynamics

Institute of Geophysics Polish Academy of Sciences

Organisation/Company Institute of Geophysics Polish Academy of Sciences Department Magnetism Research Field Geosciences Researcher Profile Recognised Researcher (R2) Positions PhD Positions Country Poland Final date to receive applications 28 Jan 2026 - 23:59 (Europe/Warsaw) Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 19 Dec 2025 Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

FORM FOR EMPLOYERS

INSTITUTION:
Institute of Geophysics Polish Academy of Sciences

CITY:
Warsaw

POSITION:
Assistant Professor (Adiunkt) in the Department of Hydrology and Hydrodynamics

DISCIPLINE:
Earth and Environmental Sciences

; field research; riverine vegetation; remote sensing; UAV/drones; GIS; environmental data analysis; machine learning; numerical modelling; tracer experiments; dispersion and mixing.

We are seeking a researcher holding a PhD degree for a scientific position of Assistant Professor (Adiunkt) within the VEGMIX project
, which focuses on the impact of natural vegetation on flow and mixing in small watercourses. The research is based on field campaigns involving tracer experiments, supported by remote sensing (including drone data), data analysis, and numerical computations. A short description of the project is available on the NCN website: (Use the "Apply for this Job" box below)..pdf

The project is interdisciplinary
, combining field studies, environmental observations, laboratory analyses, and computational data-processing methods. The project is run in cooperation with scientists from the University of Sheffield (UK) and the Finnish Environment Institute, Syke (Finland).

Impact of vegetation on flow and mixing in small watercourses (VEGMIX)
– from laboratory to natural environment NCN call type:
OPUS 27

Scope of duties:

• participation in field campaigns, including hydrodynamic measurements, tracer experiments and monitoring of environmental parameters;
• participation in processing and analysis of collected data, including the use of programming languages (Python and/or C++), GIS methods, and machine learning algorithms;
• integration of vegetation information with hydrodynamic data and tracer experiment results;
• interpretation of results in the context of physical and environmental processes;
• support in organising and conducting field campaigns, including communication with local communities and administrative institutions;
• preparation and maintenance of structured research documentation (including data descriptions and metadata);
• active participation in the preparation of scientific publications, presentation of research results, popular science activities, and dissemination of project outcomes;
• ongoing collaboration with the project leader, international partners, and the research team.

Required:

• very good command of English;
• solid background in mathematics and physics;
• experience in data analysis;
• programming skills in Python and/or C++;
• strong analytical thinking and ability to solve research problems independently;
• willingness to work regularly in the field;
• ability to work in a team.
• category B driving license (frequent field trips);
• knowledge of Polish sufficient for effective communication in the field with local communities and administrative institutions;
• experience in conducting field research or working with data collected under field conditions.

Assets (experience in one or more of the following areas):

• experience in analysing drone (UAV) data and/or working in a GIS environment;
• experience in applying machine learning algorithms;
• experience in observing aquatic or riparian vegetation;
• knowledge of vegetation functioning in the context of environmental processes (hydrological, hydraulic, ecological, and biogeochemical);
• experience in numerical modelling of flow and transport;
• laboratory experience;
• education (or research experience) in a science or engineering background, e.g., physics, applied mathematics, engineering, and willingness to work with real-world data;
• experience in operating and…