JOB DESCRIPTION

Employment status and working hours

Permanent, full time.

Full time hours at the University are 35 per week with the standard working pattern Monday to Friday from 09:00 to 17:00.

Organisation structure

Education and Students, School of Planning.

Your line manager will be Professor Samer Bagaeen, Head of School of Planning.

Place of work flexibility

You will normally spend your total working time at home*, which must be within the UK. You may, on occasions, be expected to attend the Horizons, Reading office as well as our London School of Architecture Campus in Dalston, to attend meetings or training events. This will total a minimum of six days per year and dates will be notified to you in advance. *Working from home is only possible if your environment is suitable.

High level summary of job role

As a Lecturer in Renewable Energy and AI, you will contribute to the delivery and development of the MSc programme Renewable Energy and AI, teaching across core modules in renewable energy systems, infrastructure delivery and data-driven engineering. You will deliver high-quality lectures (online), develop new modules, lead seminars and webinars, design assessments, and supervise MSc research projects.

You will have expertise in renewable energy technologies (e.g. solar and wind systems), the delivery of nationally significant infrastructure projects, with experience in geospatial data analysis, modelling, or data engineering. Proficiency in MATLAB and preferably Python is essential, as both platforms underpin the content of our core modules. You will support an innovative, interdisciplinary learning environment aligned with government infrastructure policy, industry and research needs.

Specific job role accountabilities and responsibilities

Programme development and delivery

Contribute to the development and delivery of modules within the MSc Renewable Energy and AI programme.
Design and develop new modules in renewable energy systems, critical national infrastructure, AI applications, and data-driven engineering.
Ensure curricula reflect current industry practice in solar, wind, energy storage, smart grids, micro grids, decentralised energy systems, and digital energy systems.
Integrate MATLAB and Python-based modelling, simulation, and data analysis into teaching and assessment.

Teaching, learning and assessment

Deliver high-quality teaching across renewable energy and AI-related modules through online modes.
Lead online lectures, seminars, practical labs, and webinars using applied and industry-relevant approaches.
Design inclusive, authentic assessments aligned with real-world infrastructure and renewable energy challenges.
Provide timely feedback and academic support to a diverse postgraduate student cohort.

Project supervision and academic support

Supervise MSc research projects in infrastructure, renewable energy technologies and AI-driven applications.
Support students in experimental, modelling, simulation, and data-driven research projects.
Provide academic guidance and pastoral support throughout the research lifecycle.

Industry engagement and scholarship

Maintain awareness of emerging developments in renewable energy systems and AI integration.
Engage with industry partners to ensure programme relevance and applied learning opportunities.
Contribute to scholarship, applied research, or knowledge exchange activities aligned with the programme focus.

Academic citizenship and administration

Participate in programme review, enhancement, and quality assurance processes.
Contribute to recruitment, marketing, outreach, and student engagement activities.
Support collaborative working within the programme team and across the wider School.

Other important features of the job role

Line management responsibilities: None.

Budget responsibility: None.

In this role, you will liaise with the following: Students, the Dean, Deputy Dean, Head of Schools, Programme Leaders, Lecturers, Associate Lecturers, and University Professional Services.

PERSON SPECIFICATION

E = Essential person requirement; D = Desirable person requirement

How the person requirement will be assessed:

A = Application; I = Interview; T = Test or other assessment

Please be aware that as part of onboarding processes, we will seek original documentary evidence of the relevant academic and/or professional qualifications which you include within your job application. This is all part of our comprehensive pre-employment screening which also includes checking your identity, right to work, criminal record history (basic disclosure), and three years employment history.

Qualifications and training

Degree in Renewable Energy Engineering, Electrical Engineering, Mechanical Engineering, Energy Systems, Data Science, or a closely related discipline (E, A).

Postgraduate qualification (master’s degree or equivalent) in a relevant field (E, A).

Teaching qualification or willingness to obtain one (e.g. PGCert in Higher Education) (E, A).

Doctorate (PhD) in Renewable Energy, Infrastructure or Energy Systems, Artificial Intelligence, Data Engineering, or related discipline (D, A).

Previous experience

Teaching renewable energy, energy systems, AI applications, or related subjects in higher education (E, A, I).

Proficiency in MATLAB and preferably Python programming for modelling, simulation, and data analysis (E, A, I).

Demonstrable expertise in renewable energy systems (e.g. solar, wind, energy storage) (E, A, I).

Module design, curriculum development, or programme contribution (E, A, I).

Delivering teaching in online/distance learning formats (including webinars) (E, A, I).

Supervising postgraduate research projects (E, A, I).

Assessment design, marking, and quality assurance processes (E, A, I).

Industry or applied research experience in renewable energy operations, digital energy systems, or data-driven engineering (D, A, I).

Experience integrating data analytics, modelling, or AI tools into engineering education (D, A, I).

Skills, knowledge, and aptitudes

Strong knowledge of legislation underpinning the delivery of national infrastructure and renewable energy technologies, including solar PV, wind energy, and integrated energy systems (E, A, I).

Knowledge of AI, data analytics, machine learning, or data engineering applied to energy systems (E, A, I).

Ability to teach and apply MATLAB-based modelling and simulation techniques (E, A, I).

Ability to teach and apply Python programming for energy data analysis and AI applications (E, A, I).

Strong communication, organisational, and interpersonal skills (E, A, I).

Ability to design inclusive, industry-relevant, and research-informed learning experiences (E, A, I).

Ability to supervise MSc-level research involving modelling, experimentation, or data-driven analysis (E, A, I).

Digital confidence in the use of MS Office (Teams, Outlook, Word, Excel, PowerPoint) and virtual learning environments (E, A, I).

Ability to work collaboratively within interdisciplinary academic teams and with industry stakeholders (E, A, I).

Other requirements or special requirements

Alignment to the University’s core values of Passion, Integrity, Excellence and Support; all employees are expected to demonstrate our values at work (E, I).

Commitment to delivering positive outcomes for our students; we want our students to be successful (E, I).

You must be prepared to undertake mandatory online training should you be appointed including Data Protection, Health and Safety, Safeguarding, Prevent, and EDI (E, I).

PAY AND BENEFITS

Salary: Salary range £39,000 to £47,000 per annum.

Holiday: 26 days paid holiday (rising to 28 with service) plus paid bank/public holidays plus up to five paid closure days (typically between Christmas and New Year); all per holiday year Full Time Equivalent. Our holiday year runs from 1 August to 31 July. We also have a holiday buy and sell scheme in place. Sometimes the University does not need to close for five days per year and any balance (for example one day), can be used as a paid Wellbeing Day to take time out for your own physical or mental health.

Pension and life assurance: Pensions auto-enrolment to the Universities Superannuation Scheme which is a default salary sacrifice scheme. You may opt out of salary sacrifice but remain in the scheme, or you may choose to opt out altogether.

Family-friendly policies: Policies in place for all types of family-friendly statutory leave with enhanced pay available from day one of employment for some leave types. Access to Tax-Free Childcare (Government scheme).

Wellbeing: Full access to the Employee Assistance Programme as well as the Thrive Mental Wellbeing app. Several employees are trained Mental Health First Aiders and can support staff. We have a wellbeing policy and we focus on five pillars of wellbeing.

Sustainable travel: Cycle to Work and Electric Vehicle salary sacrifice schemes.

Parking: We have locked sheds for bicycles. For cars, for Horizons based roles, we may in the future be able to offer parking on site however you must not assume this will be possible. Every now and then we may have a spare parking space become available and you can ask to join the waiting list. The University is keen wherever possible, to encourage staff to commute as sustainably as possible.

Other health related benefits: Employer-funded Health Cash Plan (Simplyhealth) and voluntary dental insurance (Unum).

Other valuable benefits:

Charity giving options available including one voluntary paid day, per annum.
You will have access to Microsoft Office 365 applications for personal use.
You will have access to range of lifestyle discounts and everyday savings.
We offer learning platforms including LinkedIn learning.

On the Join the team page of our website, you will find the full list of employee benefits.

APPLICATIONS - THINGS TO CONSIDER

For an informal discussion about the role please contact Dr Mahmoud Dhimish on m.dhimish@ube.ac.uk . For any other enquiries please contact HR on 0118 467 2454 / 2433 or email recruitment@ube.ac.uk

Did one of our employees recommend you? If you apply on the recommendation of an existing employee, please make sure to mention their name within your application.

We're passionate about sustainability and we have a five-year strategic plan: We expect job seekers to be curious about who we are and what we do. We point you towards a couple of resources about who we are and sustainability.

Closing date and next steps

We will receive applications until the advert closes on Monday 18 May 2026 at 17:00. We will not consider late applications. Our ATS does not screen applications, we humans do. Applications may be reviewed prior to the closing date and occasionally, you may be invited to interview ahead of the closing date.

Interview details

Interviews are scheduled to be carried out on Friday 05 June 2026. We'll ask you to tell us about any dates you cannot make, up to 4 weeks from the closing date; this helps us to plan interviews should you be shortlisted, prior to contacting you.

Interviews are normally carried out over Microsoft Teams or Zoom. Depending on the job role and place of work, your interview may be at our Reading or London office. Please be prepared for a two-stage interview process, held on different dates. As part of the interview process, you will likely meet a member of the Senior Leadership Team.

Note for internal candidates

If you are an existing employee, we request you inform your current line manager of your intention to apply for this role.

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Lecturer – Renewable Energy and AI