德國波茨坦氣候影響研究所2024年招聘博士后職位

德國波茨坦氣候影響研究所（The Potsdam Institute for Climate Impact Research, PIK）成立于1992年，是德國政府的科研機(jī)構(gòu)，為國際上知名的氣候變化研究單位。它對(duì)全球變化、氣候影響和可持續(xù)發(fā)展等領(lǐng)域進(jìn)行科學(xué)和社會(huì)學(xué)的研究，致力于探索地球系統(tǒng)的可承受性，提出人與自然可持續(xù)發(fā)展的相關(guān)戰(zhàn)略。

Post-doctoral position (m/f/d)

Employer

Raven51 AG (Potsdam-Institut für Klimafolgenforschung e.V. (PIK))

Location

Potsdam, Brandenburg (DE)

Salary

a collective pay scheme and associated benefits as well as a subsidized travel card or Deutschland-T

Closing date

31 Oct 2024

Post-doctoral position (m/f/d) (Position number: 43-2024 Postdoc P2F) in the field of Machine Learning for Emulation of Earth system models, starting on 01.01.2025.

The position is funded for two years. Remuneration is in accordance with the German public tariff scheme (TV-L Brandenburg), salary group E 13. This is a full-time position with a weekly working time of 40 hours per week. Appointment is conditional on approval by the funding agency. The position can be filled on a part-time basis.

The position is funded via the Horizon Europe project “Past to Future: Towards fully plaeo-informed future climate projections”, which will start on January 1st 2025. This large EU project with 24 partners aims to advance Earth system models with regard to improved reproduction of climate variability as evidenced in paleoclimate proxy archives, including model re-calibrations and development of new, efficient model components also using machine learning approaches.

Key responsibilities:

Development of hybrid modelling approaches, combining physical model components with data-driven machine learning components

Contribution to the development of a new fast Earth system model designed for paleoclimate simulations

Development of differentiable Earth system model components

Development of efficient approaches to combine process-based with data-driven models

Requirements:

PhD in applied mathematics, machine learning, physics, meteorology or related field

Experience with machine learning methods is required

Experience in developing and running Earth system models is of advantage

Experience with paleoclimate proxy data and / or Earth system model simulations is of advantage

Please apply by 31.10.2024 directly using our application form.

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