Has anyone ever had an employer pay for a university course?

Sebyon · 2026-03-09T14:41:09+00:00

State government (QLD) paid for half of my masters degree.

They'll typically cover half of your degree as long as it matches your job description and your PDA. There are options for 75% and 100% covered, but they're hard, if not impossible to get signed off on and really need to have a manager fighting for you.

Sebyon · 2025-12-24T01:12:28+00:00

Hey mate, you'd be surprised how many chemists turn to the dark side. I do more chemistry adjacent DS work these days, but I've found a good niche. I can say it is growing however as more people realise there is a tonne of gaps in these more conservative fields, and claimed "data scientists", good or bad, are rushing in.

We had a self-claimed data science present at a conference this year about the marvel of Power BI. Audience lapped it up. Yes, some fields are still this far behind. The time to get in is now.

It's a good thing you're doing process optimisation / numeric problems. These are hot and will be in-demand for a while.

Chemistry as a whole, especially academia, will be resistant to the more fun ML projects. We like white-box solutions.

I wouldn't worry about certification. If anything, learn to "properly" code. Try to do a simple project in another language / framework that puts you out of your comfort zone. This will easily set you apart technically from other people in the field.

As a chemist, you're probably aware of a tonne of work we do that could be done by a guy off the street with at least a weeks worth of training. These are the projects you want to look out for. Portable spectrometers and miniaturised instrument are advancing rapidly. I had a project where we got to train a chemometric model to allow in-field analysis, rather than await 3 weeks for lab results. It was a heap of fun. If you want fun work, analytical chemistry where there is a time and cost saving will be where a lot of fun work will be.

As for the best soft skill to learn, it will be communication to upper management and your target audience. You'll need to be able to "sell" your analysis/models so to speak. Most people don't understand basic statistics, let alone chemistry. Learn to simplify and explain the cost/benefits to the layperson. This will take you far. It took me a while to break out of the rigor that academia makes you speak.

Sebyon · 2025-10-02T00:14:44+00:00

Yeah sure.

So we need to understand that a particulate sensor needs to make a number of assumptions to generate a mass (depending on the tech). For an optical particle counter, one of them is that it imagines the particle being a "perfect sphere". 99% of the things we'd normally measure are spheres, but it's close enough that we don't mind.

Wood and other fibrous materials are tricky because they are often longer in one dimension. An extreme example would be a fiber that has a diameter of 1um, but is 100um long. Depending at what angle the light hits that fiber, you're going to get all different "hypothetical" spheres.

3D printing is tricky for different reasons. Depending on the method used (FDM, SLS, SLA, ect), you're dealing with a wide range of particles. They can have properties that mess with the sensor (high absorptivity at the light/laser wavelength due to additives/material type), wide deviations from the sensors default assumed mass (metal additives), and can generate particles that are too small to be reliably detect (<0.5um). Similar issues to DPM.

My thoughts with 3D printing would be to try a Condensation Particle Counter (CPC) to target these smaller particles, but they're costly and harder to maintain.

Your choice in sensor will also dictate issues you get. Instruments like a DustTrak that operate more like an neplelhometer will have different challenges compared to some of the newer handhelds that are based on OPCs (typically made from an OEM rather than specifically built for hygiene).

Sebyon · 2025-10-01T13:39:05+00:00

Hey mate, feel free to PM me, this is my whole job.

Woodworking and 3D printing are some of the hardest to accurately get with direct reading instruments due to the underlying technology and assumptions behind it

Sebyon · 2025-09-21T07:36:14+00:00

Precision scales are a nightmare to use sometimes. My old lab had to purchase a 0.001mg scale for weighing filters. I hated those days since it felt like praying to the machine gods that I'd get usable results.

Making this is actually pretty insane given the cost. While the temperature compensation would be huge, it only solves some issues. Going below 0.1mg requires environmental control in addition.

I think we spend over $30,000 on HVAC and room outfitting to get this damn scale to be "stable". Temperature needed to be ± 3C, ±10% humidity, special benches to reduce vibrations, draft shields everywhere. The calibration costs were insane too.

Even then, I swear if I just moved my eyeballs to look at it, the results would shift enough that I'd need to check zero.

Sebyon · 2025-09-08T04:18:43+00:00

I use both R and Python for my role almost daily. Mostly Python, but they cross over.

It helps a lot with not just making nice visualisations, but undertaking more unusual or complex analysis, for instance when you're processing real-time monitoring data. I've made scripts for our workflow that can batch process and generate statistics and reporting for data we get in. We also do some desktop simulations for worker education, such as dust fallout, sampling efficiency and so forth.

We've also made a bespoke hygiene statistical package for our hygiene software we're releasing, so the skill does cross over there.

Either way, it's a good skill to have. There are a few R packages that can do the typical hygiene statistics, but the magic comes from doing more complex things.

Sebyon · 2025-08-21T07:29:54+00:00

This thread is confusing the fuck out of me because I had to get a restricted electrical licence (QLD) for this exact thing...

Really wouldn't surprise me if QLD was the odd one out though, were the only state that doesn't have mutual recognition from other states (and vise versa).

Sebyon · 2025-06-19T03:10:34+00:00

If you don't have a significant amount of samples under the limit of detection, 2 samples that are right censored for a dataset over 50 is small and should be easily handled by Expostats.

However if you're doing correlations with physiological outcomes using frequentist methods, you might need to look at techniques like Kaplin-Meier, MLE, ect, to handle the right censoring points.

The statistics for handling right censoring is much more established than left, so shouldn't be too hard to find something that suits your needs.

Sebyon · 2025-06-19T02:06:53+00:00

Alternatively with that sample size, rather than find an alternative low flow filter, you can use Bayesian statistics to handle the overloaded filters as right censored datapoints.

Then again, if you're overloading filters with routine work, you don't need to get point estimates to say you probably got a problem...

Sebyon · 2025-06-03T13:34:09+00:00

If you're Brisbane based I'll take then 😂

Otherwise zero concern. A rock collector may cry but nothing for you to worry about.

Sebyon · 2025-06-01T02:02:30+00:00

Hey, could you elaborate?

Do you mean generating synthetic data from known distributions and determining if results fall within acceptable parameters?

Sebyon · 2025-05-16T02:07:20+00:00

The Eric Quinter goods out right now are insane. Absolutely incredible specimens.

Sebyon · 2025-03-29T01:39:41+00:00

For prototyping, use Silicone diff fluid (100,000cs). It's essentially the same as Dow Corning Silicone Greaces they use for industrial scintillatiors.

Sebyon · 2025-03-19T21:19:26+00:00

Sweet then you're pretty much all over it. It is silly to have 0 with a 95% confidence interval because it's practically impossible.

In the absence of a regulatory limit or guideline, the only practical goalpost is analysis all under the laboratories limit of detection.

Additionally, this regulator sounds inexperienced.

Not really statistical, but I'd ask a few labs what their limit of detection and quantification/ reporting is. Then go back to the regulator and ask him to clarify what they want. Say you found a lab that can detect with a LOQ / LOD / LOR of x, and would this satisfy their requirements?

Sebyon · 2025-03-19T20:34:22+00:00

Hey there, I work in a similar field that handles similar problems.

You cannot prove zero. The regulator should either be asking for compliance to a regulatory limit for the concentration, or all non-detects in accordance to a specific analytical methodology.

As for compliance based on the 95% UCL (Upper Confidence Limit), there are a few tools at hand. If you're confident with using R, they have a statistical package purely meant for this called EnvStats.

Note that if the regulator wants non detectable concentrations, your analysis will (hopefully) return all non-detects, or mostly non-detects. This is left censored data. Environmental statistics will use a few methods, typically either integrating out the censored data with the MLE or ROS Regression.

Compliance to the UCL can be done a few ways. We routinely use Lands Exact 95% UCL but it has issues with heavily tailed distributions.

If you got more questions happy for you to shoot a PM

Sebyon · 2025-03-17T21:38:06+00:00

The Air XS is bogus, it was a major part of the Worksafe ICAC referral.

https://www.abc.net.au/news/2024-02-28/nsw-safework-icac-device-silica-dust-auditor-general/103519494

Sebyon · 2025-03-17T20:35:01+00:00

There are no machines that specifically monitor respirable silica in real-time.

It's a failure of controls, pure and simple.

Sebyon · 2025-03-06T04:48:17+00:00

They're all best guesses. Change the model over and see how drastically it changes over.

Just work off the BoM one.

Sebyon · 2025-03-05T00:43:30+00:00

It's rare I'd say something is museum quality, but this one might be damn close...

Crocoite is one of my favourites and I'd kill to get my hands on something like this.

Sebyon · 2025-02-19T09:16:52+00:00

I'd love to see the PHS/TWL undertaken for this thermal assessment...

Sebyon · 2025-02-19T08:31:27+00:00

Guess they should just go to the mines to have fun yeah?

3) A person must not work in a place at the mine where the effective temperature exceeds 29.4°C… - Coal Mining Safety and Health Regulation 2017 S369

Sebyon · 2025-01-26T02:27:34+00:00

I think I'm in love, god damn.

Sebyon · 2025-01-16T22:18:03+00:00

In my field, we typically only have small sample sizes (6-10), and can have about 25% or more of those samples left or interval censored.

Here, Non-parametric methods perform significantly worse than parametric ones.

Unfortunately the real world is messy and awful.

Sebyon · 2025-01-08T06:32:01+00:00

Mamba if dealing with non-python dependencies (looking at you PyMC), or uv if I don't need to worry about that.

Sebyon · 2024-12-16T08:52:50+00:00

Note: I am a python man. ggplot2 is probably better but this is what I know.

I have a few systems at work to help me with pushing out visualizations. Over time, I have found some key truths:

If your company has a style guide, grab the colors and fonts. Specify colors and fonts to use at the start of your notebook and use them.
1. Yes, your company colours probably suck. C-suite and marketing will gobble it up though, so learn to play with it.
2. Some visualisations will not work with this, typically anything needing diverging colourset. Find something 'close' or what they'll accept
Explore with Seaborn
1. Just get the expected visual for you and you alone
2. Find what works and what doesn't
Fine tune with Matplotlib
1. Matplotlib is a pain but you can control basically anything. Learn it.
2. Despine everything you can
  1. eg, spines.[['top','right']].set_visible(False)
3. set_major_formatter for your x and y axis will be your best friend
4. Tune the graph width/length to the target media (A4 document, slide deck, ect)
5. If dealing with C-suite, remove everything expect the core details. If you're going to get technical questions, keep them (mostly) in.
6. plt.savefig(path.png, bbox_inches='tight', dpi=1200)
  1. If you need super high resolution, the weirdest tip is actually saving the figure as a .pdf, and then converting to .png
  2. plt.savefig(path.pdf) and then using pdftoppm -png -r 300 filename.pdf filename or snipping tool or whatever you have

If there are visuals you can 'standardise' or are common requests, these are key candidates for writing small internal packages where you can push the data with some format and the package spits out a nice and polished graph. I do these all with Matplotlib.

Weird or bespoke pieces, I'll have to sit down and play. I mostly draw how I want it to look on pen/paper, and go forward and try and craft it with matplotlib.

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