At a Glance
- Tasks: Support innovative research in ML/AI for thermal comfort prediction in smart buildings.
- Company: Swansea University, a vibrant research-led institution with a stunning waterfront campus.
- Benefits: Flexible part-time hours, enriching career opportunities, and a great work-life balance.
- Other info: Collaborate with regional partners and contribute to meaningful academic outputs.
- Why this job: Join a cutting-edge project that impacts sustainable building management and climate innovation.
- Qualifications: Experience in Python-based machine learning and interest in explainable AI or smart buildings.
The predicted salary is between 30000 - 40000 € per year.
Swansea University is a research‑led university that has been making a difference since 1920. The University community thrives on exploration and discovery and offers the right balance of excellent teaching and research, matched by an enviable quality of life. Our stunning waterfront campuses and multicultural community make us a desirable workplace for colleagues from around the world. Our reward and benefits, and ways of working enable those who join us to have enriching careers, matched by an excellent work‑life balance.
About The Role
The School of Management at Swansea University is seeking to appoint a part‑time Research Assistant to support the project “Explainable ML‑based Predictive Model for Thermal Comfort for Net Zero Buildings”. The project aims to develop a data‑efficient and explainable machine learning framework to predict thermal comfort and support smarter, lower‑carbon building management in the Swansea Bay City Region.
- Prepare and analyse indoor environmental quality and occupant comfort data, including sensor measurements and short comfort survey data.
- Clean, harmonise, and quality‑assure datasets; address missing values, sparse comfort labels, and class imbalance.
- Apply data augmentation methods such as SMOTE or GANs where appropriate.
- Develop, train, optimise, and evaluate compact machine learning models, including Gradient Boosting, Support Vector Regression, and shallow neural networks.
- Contribute to robust cross‑validation, Bayesian optimisation, reproducible model development, and SHAP‑based explainability to identify the influence of factors such as air temperature, mean radiant temperature, relative humidity, air speed, clothing insulation, and metabolic rate on thermal comfort.
- Support the development of a Building Management System‑ready advisory prototype, including clear visual outputs and practical recommendations on comfort‑preserving setpoints and schedules.
- Contribute to project deliverables, technical reporting, academic outputs, stakeholder engagement, and knowledge exchange activities with regional partners, including building management, housing, and energy‑sector stakeholders.
This role would suit a motivated candidate with experience in Python‑based machine learning, real‑world sensor or time‑series data, and an interest in explainable AI, smart buildings, thermal comfort, or net‑zero innovation. The post is fixed‑term, part‑time for 10 months and based at Swansea University’s Bay Campus.
Research Assistant in ML/AI-based Thermal Comfort Prediction in Swansea employer: Swansea University
Swansea University is an exceptional employer, offering a vibrant and inclusive work environment that fosters exploration and innovation. With its stunning waterfront campus and a commitment to work-life balance, employees enjoy enriching careers alongside opportunities for professional growth in cutting-edge research areas like machine learning and sustainable building management. The university's collaborative culture and focus on community engagement make it a desirable workplace for those looking to make a meaningful impact.
StudySmarter Expert Advice🤫
We think this is how you could land Research Assistant in ML/AI-based Thermal Comfort Prediction in Swansea
✨Tip Number 1
Network like a pro! Reach out to current or former employees at Swansea University on LinkedIn. A friendly chat can give us insider info about the role and help you stand out when applying.
✨Tip Number 2
Show off your skills! Prepare a mini-project or a portfolio showcasing your experience with Python-based machine learning and data analysis. This will demonstrate your hands-on abilities and passion for the field.
✨Tip Number 3
Practice makes perfect! Get ready for interviews by rehearsing common questions related to ML/AI and thermal comfort. We recommend using the STAR method to structure your answers and highlight your relevant experiences.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you’re genuinely interested in being part of the Swansea University community.
We think you need these skills to ace Research Assistant in ML/AI-based Thermal Comfort Prediction in Swansea
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Research Assistant. Highlight your experience with Python-based machine learning and any relevant projects you've worked on, especially those related to thermal comfort or smart buildings.
Craft a Compelling Cover Letter:Your cover letter should tell us why you're passionate about this project. Share your interest in explainable AI and how your skills can contribute to developing a predictive model for thermal comfort.
Showcase Relevant Skills:Don’t forget to mention your experience with data cleaning, quality assurance, and machine learning models. We want to see how you can apply these skills to real-world sensor data and contribute to our project.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and get to know you better!
How to prepare for a job interview at Swansea University
✨Know Your ML Basics
Make sure you brush up on your machine learning fundamentals, especially those relevant to thermal comfort prediction. Be ready to discuss concepts like Gradient Boosting and Support Vector Regression, as well as data augmentation techniques like SMOTE or GANs.
✨Showcase Your Data Skills
Prepare examples of how you've handled real-world sensor or time-series data in the past. Discuss any experience you have with cleaning datasets, addressing missing values, and ensuring data quality. This will demonstrate your practical skills and understanding of the role.
✨Understand Explainable AI
Since the project focuses on explainable ML, be prepared to talk about SHAP-based explainability and how it can influence thermal comfort predictions. Showing that you grasp the importance of transparency in AI will set you apart from other candidates.
✨Engage with Stakeholders
Think about how you would communicate technical findings to non-technical stakeholders. Prepare to discuss how you would contribute to stakeholder engagement and knowledge exchange activities, as this is crucial for the role and the project's success.
