career-materials

name	career-materials
description	Use when preparing postdoc applications, research statements, cover letters, CV bullets, interview answers, short talks, emails, or role-fit analysis for HEP, LHCb, trigger/software, data science, or research-software roles.

Career Materials Skill

Position the profile through evidence, not adjectives. Do not inflate claims or make the candidate sound generic.

Core Profile

Use when relevant:

My profile sits at the analysis-software interface: LHCb physics analysis, Allen trigger development, CUDA performance, and ML inference integration.

For non-academic roles:

I build and validate performance-sensitive scientific software where correctness, reproducibility, and throughput all matter.

Emphasize / Avoid

Emphasize LHCb PhD work, B+ analysis where analysis fit matters, Allen HLT1 work where trigger/software fit matters, CUDA/C++, GPU-resident reconstruction, ML inference integration under framework constraints, validation against references, benchmarking, reproducible workflows, and large-collaboration work.

Avoid unsupported leadership claims, "expert" unless clearly backed, generic ML/software language, uncertain results presented as finished, publication claims not in evidence, long autobiographical openings, and inflated words like innovative, cutting-edge, passionate, world-class, or impactful.

Use a direct evidence-based tone: "I developed", "I implemented", "I validated", "I contributed to", "This role fits because".

Document Patterns

Cover letter:

1. Opening fit: role, strongest overlap, why this profile.
2. Current PhD work.
3. Role-specific technical match.
4. Collaboration and future direction.
5. Short closing.

Research statement:

1. Research identity.
2. Physics analysis experience.
3. Trigger/software and reconstruction work.
4. Methods and validation culture.
5. Proposed postdoc direction.
6. Long-term direction.

CV bullets:

• Concrete action plus domain/context.
• Mention validation, constraints, or outcome when true.
• Avoid vague bullets such as "worked on ML" or "helped with software".

Short talks:

• Fewer slides than feels comfortable.
• One message per slide.
• Prefer fit-to-role over full PhD history.
• Keep risky technical depth available verbally instead of overloading slides.

Supervisor emails:

1. Polite opening.
2. Specific role/topic.
3. One sentence on relevant background.
4. One sentence asking for a short chat or confirming interest.
5. Close.

Interview Prep

Prepare answers in three lengths: 20 seconds, 60 seconds, and 3 minutes.

Cover: why this role/group, PhD contribution, hardest technical problem, what failed, what you would change, analysis experience, validation in physics software, collaboration style, and postdoc direction.

For risk questions:

1. Acknowledge the boundary.
2. Connect adjacent experience.
3. Explain ramp-up.
4. Give evidence that ramp-up is realistic.

Role-Fit Review

Classify:

• Strong fit.
• Partial fit.
• Potential risk.
• How to position.
• Questions to ask.

Check whether the role values physics analysis, trigger, reconstruction, detector upgrade, GPU/software, ML, operations/service work, publication record, or experiment-specific experience.

For industry, translate without losing rigor: physics goals become correctness constraints, Allen/CUDA work becomes performance-critical systems work, and analysis becomes validated large-dataset work.

Output Formats

Fit assessment:

- ...

Strengths to emphasize:

- ...

Risks to handle:

- ...

Suggested positioning:

- ...

For generated text, provide the draft plus short notes on assumptions, risky claims, and what evidence would strengthen it.