UCD Post-Doc Level 1, UCD Computer Science, 2 Years

EURAXESS Ireland; Dublin; On-site; EUR 47,000 - 54,000; Full time

Job summary

University College Dublin invites applications for a temporary post of a UCD Post-doctoral Research Fellow Level 1 within UCD School of Computer Science.

The successful candidate will lead research focused on developing a scalable, AI-driven quality control system for synoptic and climate observation data, using Python-based rule checks along with LSTM and VAE architectures to automate anomaly detection.

Qualifications

• A PhD in Data Science, AI, ML, Statistics, Atmospheric Science, or closely related quantitative discipline.
• Demonstrated expertise in machine learning methodologies, particularly time-series modelling (LSTMs) and anomaly detection (VAEs).
• Strong programming proficiency in Python and familiarity with PyTorch, TensorFlow, NumPy, pandas, scikit-learn.

Skills

Data Science

Artificial Intelligence

Machine Learning

Statistics

Python

Communication skills

Oral and written communication

Education

PhD in Data Science, Artificial Intelligence, Machine Learning, Statistics, Atmospheric Science, or closely related quantitative discipline

Tools

PyTorch

TensorFlow

NumPy

pandas

scikit-learn

Job description

Organisation/Company University College Dublin Research Field Computer science » Other Computer science » Other Researcher Profile Recognised Researcher (R2) Application Deadline 25 Sep 2026 - 12:39 (UTC) Country Ireland Type of Contract Temporary Job Status Full-time 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

Offer Description

Applications are invited for a temporary post of a UCD Post-doctoral Research Fellow Level 1 within UCD School of Computer Science.

Applications are invited for a Post-doctoral Research Fellow Level 1 position within the Aimsir Centre for Artificial Intelligence for Meteorological Services Innovation and Research, based in the UCD School of Mathematics and Statistics. The successful candidate will lead research focused on developing a scalable, AI-driven quality control (QC) system for synoptic and climate observation data.

With the rapid expansion of the observational network, encompassing over 100 stations reporting one-minute data and an additional 200 stations expected soon, traditional human-led QC processes are no longer practical. This project aims to automate anomaly detection (identifying missing, stuck, and suspicious data) at high temporal resolution (five-minute intervals) by combining Python-based rule-based checks with advanced machine learning architectures, specifically Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAEs). The researcher will use historical QC archives dating back to the 1990s for model training, systematically addressing inconsistencies in legacy human-labelled datasets. Additionally, the project will investigate global QC standardisation methodologies and their integration within the EU MetNet IoT framework to optimise resource allocation and enhance real-time meteorological data reliability.

PD1 Salary Range: €47,273 - €53,925 Per Annum
Appointment on the above range will be dependent upon qualifications and experience.

Closing date: 12:00 noon (local Irish time) on 25 September 2026.

UCD do not require assistance from Recruitment Agencies. Any CV's submitted by Recruitment Agencies will be returned.

The PD1 position is intended for early stage researchers, either just after completion of a PhD or for someone entering a new area for the first time. If you have already completed your PD1 stage in UCD or will soon complete a PD1, or you are an external applicant whose total Postdoctoral experience, inclusive of the duration of the advertised post, would exceed 4 years, you should not apply and should refer to PD2 posts instead.

Communication and interpersonal skills, Data Science, Artificial Intelligence, Machine Learning, Statistics, Atmospheric Science, LSTMs, VAEs, PyTorch, TensorFlow, NumPy, pandas, scikit-learn, spatio-temporal datasets

Additional Information

Eligibility criteria

Mandatory:

• *A PhD in Data Science, Artificial Intelligence, Machine Learning, Statistics, Atmospheric Science, or a closely related quantitative discipline.
• *Demonstrated expertise in machine learning methodologies, particularly time-series sequence modelling (e.g., LSTMs) and deep generative/unsupervised anomaly detection techniques (e.g., VAEs).
• *Strong programming proficiency in Python and familiarity with standard data science and machine learning libraries (e.g., PyTorch, TensorFlow, NumPy, pandas, scikit-learn).
• *A publication record in peer-reviewed journals or top-tier scientific conference proceedings appropriate to career stage.
• *Excellent written and oral communication skills in English, with a demonstrated ability to communicate complex mathematical and machine learning concepts to technical and non-technical audiences.
• *A demonstrated commitment to research and publications
• *An understanding of the operational requirements for a successful research project
• *Evidence of research activity (publications, conference presentations, awards) and future scholarly output (working papers, research proposals, and ability to outline a research project.
• *Excellent Communication Skills (Oral, Written, Presentation etc).
• *Excellent Organisational and Administrative skills including a proven ability to work to deadlines.
• *Candidates must demonstrate an awareness of equality, diversity and inclusion agenda.

The PD1 position is intended for early stage researchers, either just after completion of a PhD or for someone entering a new area for the first time. If you have already completed your PD1 stage in UCD or will soon complete a PD1, or you are an external applicant whose total Postdoctoral experience, inclusive of the duration of the advertised post, would exceed 4 years, you should not apply and should refer to PD2 posts instead.

• *Experience working with spatio-temporal datasets, high-frequency observational time series, or meteorological/climatological datasets.
• *Prior experience in developing automated quality control (QC) pipelines, data curation workflows, or handling noisy, imperfectly labelled historical data.
• *Practical knowledge of software engineering best practices, containerised deployment, or scalable data architectures.
• *Familiarity with international meteorological data standardisation initiatives or IoT frameworks (e.g., the EU MetNet IoT framework).
• *Demonstrated capacity for collaborative, interdisciplinary research and an interest in mentoring postgraduate students within the research group.
• *Experience in setting own research agenda.