Computer Vision Research Assistant

Job Description


  • assisting in conducting research activities related to computer vision, including literature reviews, data collection, experimentation, and analysis

  • assisting in the development and implementation of computer vision algorithms, including image processing, object detection, recognition, segmentation, and tracking

  • preparing and annotating datasets for training and evaluation purposes, ensuring data quality and relevance to research objectives

  • contributing to the solution in a form of software tools and frameworks for computer vision research, using programming languages such as Python or C/C++

  • assisting in the analysis of qualitative and quantitative data, as directed.



Qualifications

N/A



Skills

  • Some prior experience and strong interest in the subject of Computer Vision

  • Understanding of deep learning frameworks (e.g., TensorFlow Keras, PyTorch) and some proficiency in training convolutional neural networks (CNNs) for computer vision tasks.

  • Familiarity in training deep learning models using preprocessed and augmented datasets, monitoring model performance and convergence during training.

  • Practical knowledge in utilizing programming languages relevant to machine learning, deep learning and computer vision (Python 3.4 and above is an absolute must).

  • Experience working with video / image data, including data preprocessing, annotation and analysis using popular libraries (e.g. OpenCV)

  • Knowledge of common evaluation metrics for assessing model performance in computer vision tasks, such as accuracy, precision, recall, and F1 score.

  • Knowledge in web frameworks written in Python (e.g. Flask) is desirable but not essential

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This is an exciting part-time position at the heart of the Child and Adolescent Mental Health Services (CAMHS) Digital Lab led by Dr Johnny Downs (https://www.camhsdlab.co.uk/).  The CAMHS Digital Lab is a recently formed team within the King’s Maudsley Partnership. The lab will enable KCL researchers, engineers, and designers with SLaM healthcare and digital service providers to work together to develop products which will improve child and adolescent mental health. The post-holder will primarily work alongside the lab’s clinical informatics lead and research platform engineer.



 



We are seeking a junior developer to join our interdisciplinary team and to support programming projects primarily relating to the optimisation of myHealthE processes.



 



The myHealthe (MHE) platform is a Digital Health Monitoring System which provides young people and their families, researchers and NHS clinical services the ability to track mental health symptoms of young people over time. It also provides a mechanism for the NHS Trusts to know which families are happy to be contacted by approved researchers for potential enrolment in research studies – a consent for research contact register. The MyHealthE programme is used with South London and Maudsley NHS Foundation Trust (SLaM) Child and Adolescent Mental Health Services (CAMHS) and will be extended across NHS Trusts in London, Nottingham, Sussex and Hampshire offer. To evaluate is applicability to support trial recruitment, MHE is used a platform to help families recently referred to NHS CAMHS to rapidly access online parenting support.



 



The successful candidate will be someone who is motivated to improve child and adolescent mental health care. They will be positive and professional. The post requires someone with excellent interpersonal and communication skills and have an interest in how programming can enhance processes to improve patient outcomes.



Qualifications

 Grade 7 in computer science



Skills

1.           Strong IT skills and knowledge of the whole MS Office suite. 



2.           Excellent communication and interpersonal skills.



3.           Produces work with an eye to accuracy and excellent attention to detail.



4.           Enthusiasm and can-do attitude with a strong work ethic, using initiative and creativity to address challenges.



5.           The ability to apply programming skills to operational service challenges



6.           Assist with developing methods for harmonization and curation of health data from diverse data sources using establish open source solutions and standards



 

The Facilities Management Logistics Team at KCL is seeking a motivated, highly organised and people orientated driver to carry out collections and deliveries at all KCL sites.



 



As a logistics driver, the role will encompass working across all KCL campuses primarily collecting/delivering items, including supporting the movement of books between 6 University Libraries at the following locations:



 



Maughan Library (Holborn)



FWB Library (Waterloo)



St Thomas Library (Waterloo)



Guys Library (London Bridge)



IOP Library (Denmark Hill)



WEC Library (Denmark Hill)



 



In line with the University’s Sustainability Strategy, our fleet of vehicles are electric, and the successful applicant will be responsible for driving one of those vehicles ensuring that they adhere to all related safe working practices, guidance, and University policies. The appointed candidate will also be responsible for reporting any damage, accidents, and any safety-related defects immediately to the line manager.



This role will also cover the transport of ad-hoc items and will cover the post room as required.



While the role of the driver is mostly carried out independently, it is also important that you are a team player, with good levels of customer service, ensuring all interactions with stakeholders are delivered to the highest calibre.



This role will report to the Fleet coordinator. 




Qualifications

Driving Licence 



Higher Education



Skills

Driving 



Manual handling

Leading English speaking sessions with international students - using questions to help guide the conversation.



Must be available to lead sessions on Fridays (daytime). 



This is an ONLINE position. No travel required.




Qualifications

N/A



Skills

None - training provided.



 

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