Merge branch 'main' of github.com:mlatcl/mlatcl.github.io

Author: lawrennd

README.md

@@ -15,3 +15,4 @@ Pages are structured into
  - _people    Our researchers and students
  - _publications  Our publications
  - _availableprojects  Part II/Part III/MPhil projects proposals and archive
+

_availableprojects/self-adaptive-systems-and-llms.md

@@ -1,7 +1,7 @@
 ---
-month: 06
+month: 07
 layout: project-to-supervise
-status: Hidden
+status: Available
 categories:
   - prtii
   - prtiii
@@ -10,11 +10,11 @@ supervisors:
   - christian-cabrera
   - neil-d-lawrence
 projects:
-  - autoai
+  - s4
 student_learn: >-
   You will learn about autonomous computing and self-adaptive systems, their architecture,
   and how they are used to solve problems in different domains and scenarios.
-published: 2024-06-07
+published: 2025-06-07
 title: Self-Adaptive Systems and Large Language Models (LLMs)
 overview: "Software systems are increasingly complex and include different actors and components interacting in
 dynamic environments. Maintaining such systems is a difficult task where human intervention is not feasible.
@@ -35,5 +35,5 @@ project_objective: "Rapid advances in Large Language Models (LLMs) open new oppo
 project_bigger_picture: "This project is part of the [Self-Sustaining Software Systems (S4)](https://arxiv.org/abs/2401.11370) 
   research agenda. The goal behind S4 is to enable a new concept for adaptable systems. S4 aims to build knowledge loops
   between different knowledge sources to improve their adaptability."
-year: 2024
+year: 2025
 ---

_data/resources.yml

@@ -1,6 +1,10 @@
 hero:
   title: Resources
 resources:
+  - title: Machine Learning Foundations Course
+    icon: /assets/images/teaching-icon.svg
+    category: Teaching
+    link: https://mlatcl.github.io/mlfc/
   - title: Machine Learning and the Physical World lecture course
     icon: /assets/images/teaching-icon.svg
     category: Teaching
@@ -41,6 +45,7 @@ resources:
     category: Report
     link: https://mlatcl.github.io/papers/autoai-sra.pdf
     icon: /assets/images/report-icon.svg
-    
-      
-      
+  - title: ML@CL Library of Libraries
+    category: Software
+    link: https://github.com/mlatcl/library
+    icon: /assets/images/software-icon.svg

_people/radzim-sendyka.md

@@ -2,14 +2,20 @@
 layout: person
 given: Radzim
 family: Sendyka
-student: True
+crsid: rs2071
+supervisor: ndl21
+start: 2023-09-01
+end: ""
+website: "https://radzim.github.io"
+orcid: 0009-0002-4072-2828
+position: Machine Learning Engineer
+institution: Cambridge University
+image: radzim-sendyka.jpg
 url: 
 orcid:
 twitter: 
-github: 
+github: radzim
 linkedin: 
-start: 2023-09-01
-crsid: rs2071
-supervisor: ndl21
+biography: |
+  Radzim is a Machine Learning Engineer in the Department of Computer Science and Technology, where he explores the practical applications of data science and machine learning in real-world contexts, with emphasis on collaboration with domain experts from various scientific fields, like Assyriology.
 ---
-

_people/sarah-morgan.md

@@ -6,6 +6,7 @@ crsid: sem91
 position: Departmental Early Career Academic Fellow, Accelerate Programme
 institution: Cambridge University
 twitter: sarah_morgan_uk
+linkedin: sarah-morgan-56137576/
 team: True
 priority: 4
 image: sarah-morgan.jpg
@@ -20,4 +21,6 @@ publications:
   - multimodal-graph-coarsening-for-interpretable-mri-based-brain-graph-neural-network
 ---
 
+Sarah was a Departmental Career Acceleration Fellow under the Accelerate Science Programme. She remains an affiliated lecturer in the Computer Lab and has accepted a position as an Assistant Professor at King's College, London.
+
 Sarah's research applies machine learning, network science and Natural Language Processing to better understand and predict mental health conditions. A main focus is using brain connectivity derived from MRI to predict disease trajectories for patients with schizophrenia. Sarah is also interested in using transcribed speech data to perform similar prediction problems.