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Lecturer (Teaching) in Data Science & Signal Processing

UCL - Department of Electronic and Electrical Engineering (EEE)

Location: London, Hybrid
Salary: £51,474 to £60,521
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 26th July 2024
Closes: 11th August 2024
Job Ref: B04-05211

About us

The Department of Electronic and Electrical Engineering (EEE) at UCL was established by Professor Sir Ambrose Fleming, inventor of the thermionic valve and hence the founder of the discipline of electronics, in 1885 as the first department of electrical technology in England. That same pioneering tradition of innovation and excellence continues to underpin our work. We push in new directions across research, education and entrepreneurship, building on our long-established links with industry and benefitting from our loyal and supportive alumni community.

About the role

The primary purpose of the successful candidate will be responsibility for teaching on various modules in the general field of Data Analysis and Machine Learning in EEE.

Specifically, the candidate will be the module leader of the post-graduate module “Data Acquisition and Signal Processing” (ELEC00136), taught in the MSc programme on Integrated Machine Learning Systems. The module covers the following topics:

  • Introduction to Data & Data Acquisition (Fourier Transform, Acquisition from Sensors, Acquisition from the Web and Databases, Statistical sampling, Population and samples, Sampling strategies and representative samples)
  • Data Preparation (Tidy vs Messy dataset, Data cleaning, data visualization, dimensionality reduction, outliers detection, data transformation)
  • Data Exploration and Data Storage (data storage, SQL vs noSQL, hypothesis testing, EDA)
  • Networked data and bootstrapping (Networked data, graphical filtering, graphical neural network, bootstrapping)
  • Data Modelling, Analysis and Processing (regression, classification, forecasting, clustering)
  • Data Representation (kernel methods, dictionaries)

The candidate will also support lecturing on other modules in term 2 related to data analysis and processing, and supervise students at the undergraduate and postgraduate level for BEng/Meng/MSc projects.

The role is available from 1st September 2024 until 31st August 2025 in the first instance. Further funding to support the post may be available.

About you

Applicants should have a PhD or equivalent qualification in electronic engineering or a closely related discipline. A comprehensive understanding and specialist knowledge of data analysis, machine learning and python language is essential. Experience of teaching undergraduates and/or postgraduates in a University Electronic/Electrical Engineering, Computer Science or equivalent department is also required. The desired candidate will possess the ability to communicate clearly, both orally and in writing, with students, academics and professional services staff at all levels. Experience of supervising project students is desirable, as well as experience using virtual learning environments such as Moodle or Blackboard.

Application details:

It is essential that the following are uploaded in your application. Please upload them separately in the required document fields.

  • CV (including a list of major achievements to date)
  • Teaching statement, including consideration of the fit to EEE given the requirement to teach in the area of Data Analysis and Machine Learning at UCL
  • [optional] Research statement, including consideration of the fit to EEE at UCL

Applications close on 11th August 2024 at 23:59.

Please find a detailed job description and contact details for enquiries at the application link.

What we offer

Visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits to find out more.

Our commitment to Equality, Diversity and Inclusion

You can read more about our commitment to Equality, Diversity and Inclusion here : https://www.ucl.ac.uk/equality-diversity-inclusion/

Our department holds an Athena SWAN Bronze award, in recognition of our commitment to advancing gender equality.

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